HR_Attribution_NN
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# Read the file and load the data
library(readr)
HR_Data_NN <- read_csv("1452762979_586__HR_Employee_Attrition_Data.csv")## Parsed with column specification:
## cols(
## .default = col_double(),
## Attrition = col_character(),
## BusinessTravel = col_character(),
## Department = col_character(),
## EducationField = col_character(),
## Gender = col_character(),
## JobRole = col_character(),
## MaritalStatus = col_character(),
## Over18 = col_character(),
## OverTime = col_character()
## )## See spec(...) for full column specifications.View(HR_Data_NN)
dim(HR_Data_NN)## [1] 2940 35str(HR_Data_NN)## Classes 'spec_tbl_df', 'tbl_df', 'tbl' and 'data.frame': 2940 obs. of 35 variables:
## $ Age : num 41 49 37 33 27 32 59 30 38 36 ...
## $ Attrition : chr "Yes" "No" "Yes" "No" ...
## $ BusinessTravel : chr "Travel_Rarely" "Travel_Frequently" "Travel_Rarely" "Travel_Frequently" ...
## $ DailyRate : num 1102 279 1373 1392 591 ...
## $ Department : chr "Sales" "Research & Development" "Research & Development" "Research & Development" ...
## $ DistanceFromHome : num 1 8 2 3 2 2 3 24 23 27 ...
## $ Education : num 2 1 2 4 1 2 3 1 3 3 ...
## $ EducationField : chr "Life Sciences" "Life Sciences" "Other" "Life Sciences" ...
## $ EmployeeCount : num 1 1 1 1 1 1 1 1 1 1 ...
## $ EmployeeNumber : num 1 2 3 4 5 6 7 8 9 10 ...
## $ EnvironmentSatisfaction : num 2 3 4 4 1 4 3 4 4 3 ...
## $ Gender : chr "Female" "Male" "Male" "Female" ...
## $ HourlyRate : num 94 61 92 56 40 79 81 67 44 94 ...
## $ JobInvolvement : num 3 2 2 3 3 3 4 3 2 3 ...
## $ JobLevel : num 2 2 1 1 1 1 1 1 3 2 ...
## $ JobRole : chr "Sales Executive" "Research Scientist" "Laboratory Technician" "Research Scientist" ...
## $ JobSatisfaction : num 4 2 3 3 2 4 1 3 3 3 ...
## $ MaritalStatus : chr "Single" "Married" "Single" "Married" ...
## $ MonthlyIncome : num 5993 5130 2090 2909 3468 ...
## $ MonthlyRate : num 19479 24907 2396 23159 16632 ...
## $ NumCompaniesWorked : num 8 1 6 1 9 0 4 1 0 6 ...
## $ Over18 : chr "Y" "Y" "Y" "Y" ...
## $ OverTime : chr "Yes" "No" "Yes" "Yes" ...
## $ PercentSalaryHike : num 11 23 15 11 12 13 20 22 21 13 ...
## $ PerformanceRating : num 3 4 3 3 3 3 4 4 4 3 ...
## $ RelationshipSatisfaction: num 1 4 2 3 4 3 1 2 2 2 ...
## $ StandardHours : num 80 80 80 80 80 80 80 80 80 80 ...
## $ StockOptionLevel : num 0 1 0 0 1 0 3 1 0 2 ...
## $ TotalWorkingYears : num 8 10 7 8 6 8 12 1 10 17 ...
## $ TrainingTimesLastYear : num 0 3 3 3 3 2 3 2 2 3 ...
## $ WorkLifeBalance : num 1 3 3 3 3 2 2 3 3 2 ...
## $ YearsAtCompany : num 6 10 0 8 2 7 1 1 9 7 ...
## $ YearsInCurrentRole : num 4 7 0 7 2 7 0 0 7 7 ...
## $ YearsSinceLastPromotion : num 0 1 0 3 2 3 0 0 1 7 ...
## $ YearsWithCurrManager : num 5 7 0 0 2 6 0 0 8 7 ...
## - attr(*, "spec")=
## .. cols(
## .. Age = col_double(),
## .. Attrition = col_character(),
## .. BusinessTravel = col_character(),
## .. DailyRate = col_double(),
## .. Department = col_character(),
## .. DistanceFromHome = col_double(),
## .. Education = col_double(),
## .. EducationField = col_character(),
## .. EmployeeCount = col_double(),
## .. EmployeeNumber = col_double(),
## .. EnvironmentSatisfaction = col_double(),
## .. Gender = col_character(),
## .. HourlyRate = col_double(),
## .. JobInvolvement = col_double(),
## .. JobLevel = col_double(),
## .. JobRole = col_character(),
## .. JobSatisfaction = col_double(),
## .. MaritalStatus = col_character(),
## .. MonthlyIncome = col_double(),
## .. MonthlyRate = col_double(),
## .. NumCompaniesWorked = col_double(),
## .. Over18 = col_character(),
## .. OverTime = col_character(),
## .. PercentSalaryHike = col_double(),
## .. PerformanceRating = col_double(),
## .. RelationshipSatisfaction = col_double(),
## .. StandardHours = col_double(),
## .. StockOptionLevel = col_double(),
## .. TotalWorkingYears = col_double(),
## .. TrainingTimesLastYear = col_double(),
## .. WorkLifeBalance = col_double(),
## .. YearsAtCompany = col_double(),
## .. YearsInCurrentRole = col_double(),
## .. YearsSinceLastPromotion = col_double(),
## .. YearsWithCurrManager = col_double()
## .. )head(HR_Data_NN)## # A tibble: 6 x 35
## Age Attrition BusinessTravel DailyRate Department DistanceFromHome
## <dbl> <chr> <chr> <dbl> <chr> <dbl>
## 1 41 Yes Travel_Rarely 1102 Sales 1
## 2 49 No Travel_Freque~ 279 Research ~ 8
## 3 37 Yes Travel_Rarely 1373 Research ~ 2
## 4 33 No Travel_Freque~ 1392 Research ~ 3
## 5 27 No Travel_Rarely 591 Research ~ 2
## 6 32 No Travel_Freque~ 1005 Research ~ 2
## # ... with 29 more variables: Education <dbl>, EducationField <chr>,
## # EmployeeCount <dbl>, EmployeeNumber <dbl>,
## # EnvironmentSatisfaction <dbl>, Gender <chr>, HourlyRate <dbl>,
## # JobInvolvement <dbl>, JobLevel <dbl>, JobRole <chr>,
## # JobSatisfaction <dbl>, MaritalStatus <chr>, MonthlyIncome <dbl>,
## # MonthlyRate <dbl>, NumCompaniesWorked <dbl>, Over18 <chr>,
## # OverTime <chr>, PercentSalaryHike <dbl>, PerformanceRating <dbl>,
## # RelationshipSatisfaction <dbl>, StandardHours <dbl>,
## # StockOptionLevel <dbl>, TotalWorkingYears <dbl>,
## # TrainingTimesLastYear <dbl>, WorkLifeBalance <dbl>,
## # YearsAtCompany <dbl>, YearsInCurrentRole <dbl>,
## # YearsSinceLastPromotion <dbl>, YearsWithCurrManager <dbl>tail(HR_Data_NN)## # A tibble: 6 x 35
## Age Attrition BusinessTravel DailyRate Department DistanceFromHome
## <dbl> <chr> <chr> <dbl> <chr> <dbl>
## 1 26 No Travel_Rarely 1167 Sales 5
## 2 36 No Travel_Freque~ 884 Research ~ 23
## 3 39 No Travel_Rarely 613 Research ~ 6
## 4 27 No Travel_Rarely 155 Research ~ 4
## 5 49 No Travel_Freque~ 1023 Sales 2
## 6 34 No Travel_Rarely 628 Research ~ 8
## # ... with 29 more variables: Education <dbl>, EducationField <chr>,
## # EmployeeCount <dbl>, EmployeeNumber <dbl>,
## # EnvironmentSatisfaction <dbl>, Gender <chr>, HourlyRate <dbl>,
## # JobInvolvement <dbl>, JobLevel <dbl>, JobRole <chr>,
## # JobSatisfaction <dbl>, MaritalStatus <chr>, MonthlyIncome <dbl>,
## # MonthlyRate <dbl>, NumCompaniesWorked <dbl>, Over18 <chr>,
## # OverTime <chr>, PercentSalaryHike <dbl>, PerformanceRating <dbl>,
## # RelationshipSatisfaction <dbl>, StandardHours <dbl>,
## # StockOptionLevel <dbl>, TotalWorkingYears <dbl>,
## # TrainingTimesLastYear <dbl>, WorkLifeBalance <dbl>,
## # YearsAtCompany <dbl>, YearsInCurrentRole <dbl>,
## # YearsSinceLastPromotion <dbl>, YearsWithCurrManager <dbl>summary(HR_Data_NN)## Age Attrition BusinessTravel DailyRate
## Min. :18.00 Length:2940 Length:2940 Min. : 102.0
## 1st Qu.:30.00 Class :character Class :character 1st Qu.: 465.0
## Median :36.00 Mode :character Mode :character Median : 802.0
## Mean :36.92 Mean : 802.5
## 3rd Qu.:43.00 3rd Qu.:1157.0
## Max. :60.00 Max. :1499.0
## Department DistanceFromHome Education EducationField
## Length:2940 Min. : 1.000 Min. :1.000 Length:2940
## Class :character 1st Qu.: 2.000 1st Qu.:2.000 Class :character
## Mode :character Median : 7.000 Median :3.000 Mode :character
## Mean : 9.193 Mean :2.913
## 3rd Qu.:14.000 3rd Qu.:4.000
## Max. :29.000 Max. :5.000
## EmployeeCount EmployeeNumber EnvironmentSatisfaction Gender
## Min. :1 Min. : 1.0 Min. :1.000 Length:2940
## 1st Qu.:1 1st Qu.: 735.8 1st Qu.:2.000 Class :character
## Median :1 Median :1470.5 Median :3.000 Mode :character
## Mean :1 Mean :1470.5 Mean :2.722
## 3rd Qu.:1 3rd Qu.:2205.2 3rd Qu.:4.000
## Max. :1 Max. :2940.0 Max. :4.000
## HourlyRate JobInvolvement JobLevel JobRole
## Min. : 30.00 Min. :1.00 Min. :1.000 Length:2940
## 1st Qu.: 48.00 1st Qu.:2.00 1st Qu.:1.000 Class :character
## Median : 66.00 Median :3.00 Median :2.000 Mode :character
## Mean : 65.89 Mean :2.73 Mean :2.064
## 3rd Qu.: 84.00 3rd Qu.:3.00 3rd Qu.:3.000
## Max. :100.00 Max. :4.00 Max. :5.000
## JobSatisfaction MaritalStatus MonthlyIncome MonthlyRate
## Min. :1.000 Length:2940 Min. : 1009 Min. : 2094
## 1st Qu.:2.000 Class :character 1st Qu.: 2911 1st Qu.: 8045
## Median :3.000 Mode :character Median : 4919 Median :14236
## Mean :2.729 Mean : 6503 Mean :14313
## 3rd Qu.:4.000 3rd Qu.: 8380 3rd Qu.:20462
## Max. :4.000 Max. :19999 Max. :26999
## NumCompaniesWorked Over18 OverTime
## Min. :0.000 Length:2940 Length:2940
## 1st Qu.:1.000 Class :character Class :character
## Median :2.000 Mode :character Mode :character
## Mean :2.693
## 3rd Qu.:4.000
## Max. :9.000
## PercentSalaryHike PerformanceRating RelationshipSatisfaction
## Min. :11.00 Min. :3.000 Min. :1.000
## 1st Qu.:12.00 1st Qu.:3.000 1st Qu.:2.000
## Median :14.00 Median :3.000 Median :3.000
## Mean :15.21 Mean :3.154 Mean :2.712
## 3rd Qu.:18.00 3rd Qu.:3.000 3rd Qu.:4.000
## Max. :25.00 Max. :4.000 Max. :4.000
## StandardHours StockOptionLevel TotalWorkingYears TrainingTimesLastYear
## Min. :80 Min. :0.0000 Min. : 0.00 Min. :0.000
## 1st Qu.:80 1st Qu.:0.0000 1st Qu.: 6.00 1st Qu.:2.000
## Median :80 Median :1.0000 Median :10.00 Median :3.000
## Mean :80 Mean :0.7939 Mean :11.28 Mean :2.799
## 3rd Qu.:80 3rd Qu.:1.0000 3rd Qu.:15.00 3rd Qu.:3.000
## Max. :80 Max. :3.0000 Max. :40.00 Max. :6.000
## WorkLifeBalance YearsAtCompany YearsInCurrentRole
## Min. :1.000 Min. : 0.000 Min. : 0.000
## 1st Qu.:2.000 1st Qu.: 3.000 1st Qu.: 2.000
## Median :3.000 Median : 5.000 Median : 3.000
## Mean :2.761 Mean : 7.008 Mean : 4.229
## 3rd Qu.:3.000 3rd Qu.: 9.000 3rd Qu.: 7.000
## Max. :4.000 Max. :40.000 Max. :18.000
## YearsSinceLastPromotion YearsWithCurrManager
## Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 2.000
## Median : 1.000 Median : 3.000
## Mean : 2.188 Mean : 4.123
## 3rd Qu.: 3.000 3rd Qu.: 7.000
## Max. :15.000 Max. :17.000Including Plots
You can also embed plots, for example:
library(dummies)## dummies-1.5.6 provided by Decision Patternslibrary(caret)## Warning: package 'caret' was built under R version 3.6.1## Loading required package: lattice## Loading required package: ggplot2## Warning: package 'ggplot2' was built under R version 3.6.1library(caTools)## Warning: package 'caTools' was built under R version 3.6.1library(ggplot2)
library(corrplot)## Warning: package 'corrplot' was built under R version 3.6.1## corrplot 0.84 loadedlibrary(scales)## Warning: package 'scales' was built under R version 3.6.1##
## Attaching package: 'scales'## The following object is masked from 'package:readr':
##
## col_factorlibrary(neuralnet)## Warning: package 'neuralnet' was built under R version 3.6.1library(data.table)Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.
attach(HR_Data_NN)
HR_Data_NN <- as.data.frame(HR_Data_NN)
class(HR_Data_NN)## [1] "data.frame"dim(HR_Data_NN)## [1] 2940 35# Removing columns that are not significant from the Dataframe
HR_Data_NN_Active <- subset(HR_Data_NN , select = -c(EmployeeCount, EmployeeNumber , Over18, StandardHours ))
dim(HR_Data_NN_Active)## [1] 2940 31 HR_Data_NN_Active$Attrition <- ifelse(HR_Data_NN_Active$Attrition=="Yes",1,0)
HR_Data_NN_Active$Attrition <- as.numeric(HR_Data_NN_Active$Attrition)
#HR_Data_NN_Active$Attrition
HR_Data_NN_Active$BusinessTravel <- as.factor(HR_Data_NN_Active$BusinessTravel)
HR_Data_NN_Active$Department <- as.factor(HR_Data_NN_Active$Department)
HR_Data_NN_Active$EducationField <- as.factor(HR_Data_NN_Active$EducationField)
HR_Data_NN_Active$Gender <- as.factor(HR_Data_NN_Active$Gender)
HR_Data_NN_Active$JobRole <- as.factor(HR_Data_NN_Active$JobRole)
HR_Data_NN_Active$MaritalStatus <- as.factor(HR_Data_NN_Active$MaritalStatus)
HR_Data_NN_Active$OverTime <- as.factor(HR_Data_NN_Active$OverTime)
str(HR_Data_NN_Active)## 'data.frame': 2940 obs. of 31 variables:
## $ Age : num 41 49 37 33 27 32 59 30 38 36 ...
## $ Attrition : num 1 0 1 0 0 0 0 0 0 0 ...
## $ BusinessTravel : Factor w/ 3 levels "Non-Travel","Travel_Frequently",..: 3 2 3 2 3 2 3 3 2 3 ...
## $ DailyRate : num 1102 279 1373 1392 591 ...
## $ Department : Factor w/ 3 levels "Human Resources",..: 3 2 2 2 2 2 2 2 2 2 ...
## $ DistanceFromHome : num 1 8 2 3 2 2 3 24 23 27 ...
## $ Education : num 2 1 2 4 1 2 3 1 3 3 ...
## $ EducationField : Factor w/ 6 levels "Human Resources",..: 2 2 5 2 4 2 4 2 2 4 ...
## $ EnvironmentSatisfaction : num 2 3 4 4 1 4 3 4 4 3 ...
## $ Gender : Factor w/ 2 levels "Female","Male": 1 2 2 1 2 2 1 2 2 2 ...
## $ HourlyRate : num 94 61 92 56 40 79 81 67 44 94 ...
## $ JobInvolvement : num 3 2 2 3 3 3 4 3 2 3 ...
## $ JobLevel : num 2 2 1 1 1 1 1 1 3 2 ...
## $ JobRole : Factor w/ 9 levels "Healthcare Representative",..: 8 7 3 7 3 3 3 3 5 1 ...
## $ JobSatisfaction : num 4 2 3 3 2 4 1 3 3 3 ...
## $ MaritalStatus : Factor w/ 3 levels "Divorced","Married",..: 3 2 3 2 2 3 2 1 3 2 ...
## $ MonthlyIncome : num 5993 5130 2090 2909 3468 ...
## $ MonthlyRate : num 19479 24907 2396 23159 16632 ...
## $ NumCompaniesWorked : num 8 1 6 1 9 0 4 1 0 6 ...
## $ OverTime : Factor w/ 2 levels "No","Yes": 2 1 2 2 1 1 2 1 1 1 ...
## $ PercentSalaryHike : num 11 23 15 11 12 13 20 22 21 13 ...
## $ PerformanceRating : num 3 4 3 3 3 3 4 4 4 3 ...
## $ RelationshipSatisfaction: num 1 4 2 3 4 3 1 2 2 2 ...
## $ StockOptionLevel : num 0 1 0 0 1 0 3 1 0 2 ...
## $ TotalWorkingYears : num 8 10 7 8 6 8 12 1 10 17 ...
## $ TrainingTimesLastYear : num 0 3 3 3 3 2 3 2 2 3 ...
## $ WorkLifeBalance : num 1 3 3 3 3 2 2 3 3 2 ...
## $ YearsAtCompany : num 6 10 0 8 2 7 1 1 9 7 ...
## $ YearsInCurrentRole : num 4 7 0 7 2 7 0 0 7 7 ...
## $ YearsSinceLastPromotion : num 0 1 0 3 2 3 0 0 1 7 ...
## $ YearsWithCurrManager : num 5 7 0 0 2 6 0 0 8 7 ... HR_Data_NN_Active## Age Attrition BusinessTravel DailyRate Department
## 1 41 1 Travel_Rarely 1102 Sales
## 2 49 0 Travel_Frequently 279 Research & Development
## 3 37 1 Travel_Rarely 1373 Research & Development
## 4 33 0 Travel_Frequently 1392 Research & Development
## 5 27 0 Travel_Rarely 591 Research & Development
## 6 32 0 Travel_Frequently 1005 Research & Development
## 7 59 0 Travel_Rarely 1324 Research & Development
## 8 30 0 Travel_Rarely 1358 Research & Development
## 9 38 0 Travel_Frequently 216 Research & Development
## 10 36 0 Travel_Rarely 1299 Research & Development
## 11 35 0 Travel_Rarely 809 Research & Development
## 12 29 0 Travel_Rarely 153 Research & Development
## 13 31 0 Travel_Rarely 670 Research & Development
## 14 34 0 Travel_Rarely 1346 Research & Development
## 15 28 1 Travel_Rarely 103 Research & Development
## 16 29 0 Travel_Rarely 1389 Research & Development
## 17 32 0 Travel_Rarely 334 Research & Development
## 18 22 0 Non-Travel 1123 Research & Development
## 19 53 0 Travel_Rarely 1219 Sales
## 20 38 0 Travel_Rarely 371 Research & Development
## 21 24 0 Non-Travel 673 Research & Development
## 22 36 1 Travel_Rarely 1218 Sales
## 23 34 0 Travel_Rarely 419 Research & Development
## 24 21 0 Travel_Rarely 391 Research & Development
## 25 34 1 Travel_Rarely 699 Research & Development
## 26 53 0 Travel_Rarely 1282 Research & Development
## 27 32 1 Travel_Frequently 1125 Research & Development
## 28 42 0 Travel_Rarely 691 Sales
## 29 44 0 Travel_Rarely 477 Research & Development
## 30 46 0 Travel_Rarely 705 Sales
## 31 33 0 Travel_Rarely 924 Research & Development
## 32 44 0 Travel_Rarely 1459 Research & Development
## 33 30 0 Travel_Rarely 125 Research & Development
## 34 39 1 Travel_Rarely 895 Sales
## 35 24 1 Travel_Rarely 813 Research & Development
## 36 43 0 Travel_Rarely 1273 Research & Development
## 37 50 1 Travel_Rarely 869 Sales
## 38 35 0 Travel_Rarely 890 Sales
## 39 36 0 Travel_Rarely 852 Research & Development
## 40 33 0 Travel_Frequently 1141 Sales
## 41 35 0 Travel_Rarely 464 Research & Development
## 42 27 0 Travel_Rarely 1240 Research & Development
## 43 26 1 Travel_Rarely 1357 Research & Development
## 44 27 0 Travel_Frequently 994 Sales
## 45 30 0 Travel_Frequently 721 Research & Development
## 46 41 1 Travel_Rarely 1360 Research & Development
## 47 34 0 Non-Travel 1065 Sales
## 48 37 0 Travel_Rarely 408 Research & Development
## 49 46 0 Travel_Frequently 1211 Sales
## 50 35 0 Travel_Rarely 1229 Research & Development
## 51 48 1 Travel_Rarely 626 Research & Development
## 52 28 1 Travel_Rarely 1434 Research & Development
## 53 44 0 Travel_Rarely 1488 Sales
## 54 35 0 Non-Travel 1097 Research & Development
## 55 26 0 Travel_Rarely 1443 Sales
## 56 33 0 Travel_Frequently 515 Research & Development
## 57 35 0 Travel_Frequently 853 Sales
## 58 35 0 Travel_Rarely 1142 Research & Development
## 59 31 0 Travel_Rarely 655 Research & Development
## 60 37 0 Travel_Rarely 1115 Research & Development
## 61 32 0 Travel_Rarely 427 Research & Development
## 62 38 0 Travel_Frequently 653 Research & Development
## 63 50 0 Travel_Rarely 989 Research & Development
## 64 59 0 Travel_Rarely 1435 Sales
## 65 36 0 Travel_Rarely 1223 Research & Development
## 66 55 0 Travel_Rarely 836 Research & Development
## 67 36 0 Travel_Frequently 1195 Research & Development
## 68 45 0 Travel_Rarely 1339 Research & Development
## 69 35 0 Travel_Frequently 664 Research & Development
## 70 36 1 Travel_Rarely 318 Research & Development
## 71 59 0 Travel_Frequently 1225 Sales
## 72 29 0 Travel_Rarely 1328 Research & Development
## 73 31 0 Travel_Rarely 1082 Research & Development
## 74 32 0 Travel_Rarely 548 Research & Development
## 75 36 0 Travel_Rarely 132 Research & Development
## 76 31 0 Travel_Rarely 746 Research & Development
## 77 35 0 Travel_Rarely 776 Sales
## 78 45 0 Travel_Rarely 193 Research & Development
## 79 37 0 Travel_Rarely 397 Research & Development
## 80 46 0 Travel_Rarely 945 Human Resources
## 81 30 0 Travel_Rarely 852 Research & Development
## 82 35 0 Travel_Rarely 1214 Research & Development
## 83 55 0 Travel_Rarely 111 Sales
## 84 38 0 Non-Travel 573 Research & Development
## 85 34 0 Travel_Rarely 1153 Research & Development
## 86 56 0 Travel_Rarely 1400 Research & Development
## 87 23 0 Travel_Rarely 541 Sales
## 88 51 0 Travel_Rarely 432 Research & Development
## 89 30 0 Travel_Rarely 288 Research & Development
## 90 46 1 Travel_Rarely 669 Sales
## 91 40 0 Travel_Frequently 530 Research & Development
## 92 51 0 Travel_Rarely 632 Sales
## 93 30 0 Travel_Rarely 1334 Sales
## 94 46 0 Travel_Frequently 638 Research & Development
## 95 32 0 Travel_Rarely 1093 Sales
## 96 54 0 Travel_Rarely 1217 Research & Development
## 97 24 0 Travel_Rarely 1353 Sales
## 98 28 0 Non-Travel 120 Sales
## 99 58 0 Travel_Rarely 682 Sales
## 100 44 0 Non-Travel 489 Research & Development
## 101 37 1 Travel_Rarely 807 Human Resources
## 102 32 0 Travel_Rarely 827 Research & Development
## 103 20 1 Travel_Frequently 871 Research & Development
## 104 34 0 Travel_Rarely 665 Research & Development
## 105 37 0 Non-Travel 1040 Research & Development
## 106 59 0 Non-Travel 1420 Human Resources
## 107 50 0 Travel_Frequently 1115 Research & Development
## 108 25 1 Travel_Rarely 240 Sales
## 109 25 0 Travel_Rarely 1280 Research & Development
## 110 22 0 Travel_Rarely 534 Research & Development
## 111 51 0 Travel_Frequently 1456 Research & Development
## 112 34 1 Travel_Frequently 658 Research & Development
## 113 54 0 Non-Travel 142 Human Resources
## 114 24 0 Travel_Rarely 1127 Research & Development
## 115 34 0 Travel_Rarely 1031 Research & Development
## 116 37 0 Travel_Rarely 1189 Sales
## 117 34 0 Travel_Rarely 1354 Research & Development
## 118 36 0 Travel_Frequently 1467 Sales
## 119 36 0 Travel_Rarely 922 Research & Development
## 120 43 0 Travel_Frequently 394 Sales
## 121 30 0 Travel_Frequently 1312 Research & Development
## 122 33 0 Non-Travel 750 Sales
## 123 56 1 Travel_Rarely 441 Research & Development
## 124 51 0 Travel_Rarely 684 Research & Development
## 125 31 1 Travel_Rarely 249 Sales
## 126 26 0 Travel_Rarely 841 Research & Development
## 127 58 1 Travel_Rarely 147 Research & Development
## 128 19 1 Travel_Rarely 528 Sales
## 129 22 0 Travel_Rarely 594 Research & Development
## 130 49 0 Travel_Rarely 470 Research & Development
## 131 43 0 Travel_Frequently 957 Research & Development
## 132 50 0 Travel_Frequently 809 Sales
## 133 31 1 Travel_Rarely 542 Sales
## 134 41 0 Travel_Rarely 802 Sales
## 135 26 0 Travel_Rarely 1355 Human Resources
## 136 36 0 Travel_Rarely 216 Research & Development
## 137 51 1 Travel_Frequently 1150 Research & Development
## 138 39 0 Travel_Rarely 1329 Sales
## 139 25 0 Travel_Rarely 959 Sales
## 140 30 0 Travel_Rarely 1240 Human Resources
## 141 32 1 Travel_Rarely 1033 Research & Development
## 142 45 0 Travel_Rarely 1316 Research & Development
## 143 38 0 Travel_Rarely 364 Research & Development
## 144 30 0 Travel_Rarely 438 Research & Development
## 145 32 0 Travel_Frequently 689 Sales
## 146 30 0 Travel_Rarely 201 Research & Development
## 147 30 0 Travel_Rarely 1427 Research & Development
## 148 41 0 Travel_Frequently 857 Research & Development
## 149 41 0 Travel_Rarely 933 Research & Development
## 150 19 0 Travel_Rarely 1181 Research & Development
## 151 40 0 Travel_Frequently 1395 Research & Development
## 152 35 0 Travel_Rarely 662 Sales
## 153 53 0 Travel_Rarely 1436 Sales
## 154 45 0 Travel_Rarely 194 Research & Development
## 155 32 0 Travel_Frequently 967 Sales
## 156 29 0 Non-Travel 1496 Research & Development
## 157 51 0 Travel_Rarely 1169 Research & Development
## 158 58 0 Travel_Rarely 1145 Research & Development
## 159 40 0 Travel_Rarely 630 Sales
## 160 34 0 Travel_Frequently 303 Sales
## 161 22 0 Travel_Rarely 1256 Research & Development
## 162 27 0 Non-Travel 691 Research & Development
## 163 28 0 Travel_Rarely 440 Research & Development
## 164 57 0 Travel_Rarely 334 Research & Development
## 165 27 0 Non-Travel 1450 Research & Development
## 166 50 0 Travel_Rarely 1452 Research & Development
## 167 41 0 Travel_Rarely 465 Research & Development
## 168 30 0 Travel_Rarely 1339 Sales
## 169 38 0 Travel_Rarely 702 Sales
## 170 32 0 Travel_Rarely 120 Research & Development
## 171 27 0 Travel_Rarely 1157 Research & Development
## 172 19 1 Travel_Frequently 602 Sales
## 173 36 0 Travel_Frequently 1480 Research & Development
## 174 30 0 Non-Travel 111 Research & Development
## 175 45 0 Travel_Rarely 1268 Sales
## 176 56 0 Travel_Rarely 713 Research & Development
## 177 33 0 Travel_Rarely 134 Research & Development
## 178 19 1 Travel_Rarely 303 Research & Development
## 179 46 0 Travel_Rarely 526 Sales
## 180 38 0 Travel_Rarely 1380 Research & Development
## 181 31 0 Travel_Rarely 140 Research & Development
## 182 34 0 Travel_Rarely 629 Research & Development
## 183 41 1 Travel_Rarely 1356 Sales
## 184 50 0 Travel_Rarely 328 Research & Development
## 185 53 0 Travel_Rarely 1084 Research & Development
## 186 33 0 Travel_Rarely 931 Research & Development
## 187 40 0 Travel_Rarely 989 Research & Development
## 188 55 0 Travel_Rarely 692 Research & Development
## 189 34 0 Travel_Frequently 1069 Research & Development
## 190 51 0 Travel_Rarely 313 Research & Development
## 191 52 0 Travel_Rarely 699 Research & Development
## 192 27 0 Travel_Rarely 894 Research & Development
## 193 35 1 Travel_Rarely 556 Research & Development
## 194 43 0 Non-Travel 1344 Research & Development
## 195 45 0 Non-Travel 1195 Research & Development
## 196 37 0 Travel_Rarely 290 Research & Development
## 197 35 0 Travel_Frequently 138 Research & Development
## 198 42 0 Non-Travel 926 Research & Development
## 199 38 0 Travel_Rarely 1261 Research & Development
## 200 38 0 Travel_Rarely 1084 Research & Development
## 201 27 0 Travel_Frequently 472 Research & Development
## 202 49 0 Non-Travel 1002 Research & Development
## 203 34 0 Travel_Frequently 878 Research & Development
## 204 40 0 Travel_Rarely 905 Research & Development
## 205 38 1 Travel_Rarely 1180 Research & Development
## 206 29 1 Travel_Rarely 121 Sales
## 207 22 0 Travel_Rarely 1136 Research & Development
## 208 36 0 Travel_Frequently 635 Research & Development
## 209 40 0 Non-Travel 1151 Research & Development
## 210 46 0 Travel_Rarely 644 Research & Development
## 211 32 1 Travel_Rarely 1045 Sales
## 212 30 0 Non-Travel 829 Research & Development
## 213 27 0 Travel_Frequently 1242 Sales
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## 2516 3 Divorced 2345 8045
## 2517 2 Single 3420 21158
## 2518 1 Married 4373 17456
## 2519 1 Single 4759 15891
## 2520 3 Married 5301 2939
## 2521 4 Single 3673 16458
## 2522 3 Married 4768 9282
## 2523 3 Divorced 1274 7152
## 2524 3 Married 4900 2721
## 2525 2 Divorced 10466 20948
## 2526 1 Divorced 17007 11929
## 2527 3 Married 2909 15747
## 2528 2 Single 5765 17485
## 2529 2 Single 4599 7815
## 2530 3 Married 2404 16192
## 2531 1 Single 3172 16998
## 2532 2 Married 2033 7103
## 2533 3 Single 10209 19719
## 2534 3 Divorced 8620 23757
## 2535 3 Divorced 2064 15428
## 2536 3 Married 4035 16143
## 2537 2 Married 3838 8192
## 2538 3 Married 4591 24200
## 2539 1 Single 2561 5355
## 2540 3 Divorced 1563 12530
## 2541 1 Single 4898 11827
## 2542 1 Married 4789 23070
## 2543 2 Married 3180 4668
## 2544 2 Married 6549 3173
## 2545 3 Single 6388 22049
## 2546 4 Single 11244 21072
## 2547 4 Divorced 16032 24456
## 2548 1 Single 2362 7568
## 2549 1 Married 16328 22074
## 2550 2 Single 8376 9150
## 2551 2 Married 16606 11380
## 2552 2 Single 8606 21195
## 2553 3 Single 2272 24812
## 2554 1 Single 2018 21831
## 2555 3 Married 7083 12288
## 2556 3 Single 4084 4156
## 2557 4 Single 14411 24450
## 2558 3 Married 2308 4944
## 2559 2 Married 4841 24052
## 2560 4 Married 4285 3031
## 2561 1 Married 9715 7288
## 2562 2 Single 4320 24152
## 2563 4 Married 2132 4585
## 2564 4 Married 10124 18611
## 2565 1 Married 5473 19345
## 2566 3 Married 5207 22949
## 2567 4 Single 16437 17381
## 2568 1 Divorced 2296 10036
## 2569 4 Divorced 4069 8841
## 2570 2 Divorced 7441 20933
## 2571 3 Married 2430 26204
## 2572 2 Married 5878 15624
## 2573 4 Single 2644 17001
## 2574 3 Divorced 6439 13693
## 2575 3 Married 2451 22376
## 2576 1 Married 6392 10589
## 2577 1 Married 9714 5323
## 2578 3 Married 6077 14814
## 2579 1 Single 2450 21731
## 2580 2 Married 9250 17799
## 2581 1 Divorced 2074 26619
## 2582 4 Married 10169 14618
## 2583 2 Married 4855 7653
## 2584 1 Married 4087 25174
## 2585 1 Married 2367 16530
## 2586 4 Single 2972 22061
## 2587 4 Married 19586 23037
## 2588 4 Married 5484 13008
## 2589 4 Married 2061 11133
## 2590 2 Married 9924 12355
## 2591 2 Single 4198 16379
## 2592 3 Single 6815 21447
## 2593 1 Single 4723 16213
## 2594 3 Single 6142 4223
## 2595 3 Married 8237 4658
## 2596 4 Divorced 8853 24483
## 2597 3 Married 19331 19519
## 2598 3 Married 2073 12826
## 2599 1 Married 5562 19711
## 2600 4 Single 19613 26362
## 2601 3 Married 3407 25348
## 2602 3 Married 5063 15332
## 2603 1 Married 4639 11262
## 2604 2 Divorced 4876 5855
## 2605 4 Married 2690 7713
## 2606 1 Single 17567 3156
## 2607 2 Married 2408 7324
## 2608 3 Married 2814 10293
## 2609 3 Married 11245 20689
## 2610 4 Married 3312 18783
## 2611 4 Divorced 19049 3549
## 2612 2 Married 2141 5348
## 2613 1 Single 5769 23447
## 2614 1 Married 4385 24162
## 2615 1 Single 5332 21602
## 2616 3 Married 4663 12421
## 2617 4 Divorced 4724 17000
## 2618 1 Married 3211 22102
## 2619 1 Married 5377 3835
## 2620 1 Divorced 4066 16290
## 2621 1 Married 5208 26312
## 2622 1 Divorced 4877 20460
## 2623 4 Single 3117 26009
## 2624 4 Single 1569 18420
## 2625 3 Married 19658 5220
## 2626 3 Divorced 3069 10302
## 2627 3 Married 10435 25800
## 2628 3 Married 4148 12250
## 2629 3 Married 5768 26493
## 2630 3 Single 5042 3140
## 2631 4 Divorced 5770 5388
## 2632 3 Married 7756 14199
## 2633 1 Married 10306 21530
## 2634 2 Married 3936 9953
## 2635 4 Single 7945 19948
## 2636 4 Married 5743 10503
## 2637 4 Married 15202 5602
## 2638 4 Divorced 5440 22098
## 2639 4 Single 3760 17218
## 2640 3 Married 3517 22490
## 2641 4 Single 2580 6297
## 2642 1 Single 2166 3339
## 2643 3 Single 5869 23413
## 2644 1 Married 8008 22792
## 2645 3 Divorced 5206 4973
## 2646 2 Married 5295 7693
## 2647 2 Married 16413 3498
## 2648 1 Divorced 13269 21981
## 2649 3 Single 2783 13251
## 2650 2 Divorced 5433 19332
## 2651 2 Single 2013 10950
## 2652 3 Married 13966 11652
## 2653 3 Married 4374 15411
## 2654 1 Divorced 6842 26308
## 2655 3 Married 17426 18685
## 2656 3 Married 17603 3525
## 2657 4 Single 4581 10414
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## 2660 4 Divorced 5505 3921
## 2661 2 Divorced 5470 25518
## 2662 4 Married 5476 22589
## 2663 1 Divorced 2587 24941
## 2664 2 Single 2440 23826
## 2665 2 Divorced 15972 21086
## 2666 3 Single 15379 22384
## 2667 3 Single 7082 11591
## 2668 1 Single 2728 21082
## 2669 4 Divorced 5368 16130
## 2670 3 Married 5347 7419
## 2671 4 Divorced 3195 4167
## 2672 3 Single 3989 20586
## 2673 3 Married 3306 26176
## 2674 4 Married 7005 3458
## 2675 3 Married 2655 11740
## 2676 2 Single 1393 24852
## 2677 4 Single 2570 11925
## 2678 2 Divorced 3537 23737
## 2679 4 Married 3986 11912
## 2680 4 Divorced 10883 20467
## 2681 2 Married 2028 13637
## 2682 4 Divorced 9525 7677
## 2683 4 Married 2929 20338
## 2684 4 Divorced 2275 25103
## 2685 4 Married 7879 14810
## 2686 4 Single 4930 13970
## 2687 4 Married 7847 6069
## 2688 3 Married 4401 17616
## 2689 3 Single 9241 15869
## 2690 3 Married 2974 25412
## 2691 4 Single 4502 7439
## 2692 3 Married 10748 3395
## 2693 3 Married 1555 11585
## 2694 3 Married 12936 24164
## 2695 3 Married 2305 6217
## 2696 2 Single 16704 17119
## 2697 3 Married 3433 17360
## 2698 3 Married 3477 18103
## 2699 2 Married 6430 20794
## 2700 1 Married 6516 5041
## 2701 1 Divorced 3907 3622
## 2702 2 Single 5562 9697
## 2703 3 Married 6883 5151
## 2704 4 Married 2862 3811
## 2705 2 Married 4978 3536
## 2706 4 Divorced 10368 5596
## 2707 1 Divorced 6134 8658
## 2708 2 Single 6735 12147
## 2709 2 Single 3295 12862
## 2710 4 Single 5238 6670
## 2711 4 Married 6472 8989
## 2712 3 Married 9610 3840
## 2713 2 Single 19833 4349
## 2714 3 Married 9756 6595
## 2715 1 Single 4968 26427
## 2716 4 Married 2145 2097
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## 2718 3 Married 8346 20943
## 2719 4 Single 3445 6152
## 2720 2 Single 2760 14630
## 2721 3 Single 6294 23060
## 2722 1 Divorced 7140 3088
## 2723 4 Married 2932 5586
## 2724 2 Single 5147 10697
## 2725 4 Single 4507 8191
## 2726 1 Single 8564 10092
## 2727 2 Married 2468 15963
## 2728 3 Married 8161 19002
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## 2730 3 Married 5294 9128
## 2731 2 Single 2718 17674
## 2732 4 Married 5811 24539
## 2733 3 Married 2437 15587
## 2734 2 Divorced 2766 8952
## 2735 1 Married 19038 19805
## 2736 2 Divorced 3055 6194
## 2737 1 Divorced 2289 20520
## 2738 3 Divorced 4001 12313
## 2739 3 Married 12965 22308
## 2740 4 Single 3539 5033
## 2741 4 Single 6029 25353
## 2742 2 Single 2679 4567
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## 2744 1 Married 2398 15999
## 2745 4 Married 5468 13402
## 2746 3 Married 13116 22984
## 2747 2 Married 4189 8800
## 2748 4 Divorced 19328 14218
## 2749 1 Married 8321 25949
## 2750 2 Divorced 2342 11092
## 2751 2 Divorced 4071 12832
## 2752 4 Single 5813 13492
## 2753 1 Married 3143 6076
## 2754 4 Married 2044 5531
## 2755 3 Single 13464 7914
## 2756 2 Single 7991 25166
## 2757 1 Married 3377 25605
## 2758 1 Married 5538 5696
## 2759 4 Divorced 5762 24442
## 2760 2 Divorced 2592 7129
## 2761 1 Married 5346 6208
## 2762 1 Single 4213 4992
## 2763 4 Divorced 4127 19188
## 2764 3 Single 2438 24978
## 2765 2 Single 6870 15530
## 2766 3 Divorced 10447 26458
## 2767 3 Single 9667 2739
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## 2769 2 Married 8926 10842
## 2770 4 Divorced 6513 9060
## 2771 3 Married 6799 22128
## 2772 2 Divorced 16291 22577
## 2773 3 Married 2705 9696
## 2774 2 Divorced 10333 19271
## 2775 1 Divorced 4448 10748
## 2776 4 Married 6854 15696
## 2777 1 Married 9637 8277
## 2778 1 Married 3591 12719
## 2779 4 Married 5405 4244
## 2780 4 Single 4684 9125
## 2781 3 Married 15787 21624
## 2782 3 Single 1514 8018
## 2783 1 Married 2956 21495
## 2784 1 Divorced 2335 3157
## 2785 3 Married 5154 19665
## 2786 2 Married 6962 19573
## 2787 4 Married 5675 19246
## 2788 4 Single 2379 19826
## 2789 4 Married 3812 7003
## 2790 4 Single 4648 26075
## 2791 3 Married 2936 6161
## 2792 3 Single 2105 5411
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## 2794 4 Divorced 2706 10494
## 2795 3 Divorced 6384 21143
## 2796 3 Single 3968 13624
## 2797 2 Single 9907 26186
## 2798 1 Divorced 13225 7739
## 2799 3 Married 3540 7018
## 2800 2 Married 2804 15434
## 2801 3 Married 19392 22539
## 2802 2 Married 19665 13583
## 2803 4 Single 2439 14753
## 2804 2 Married 7314 14011
## 2805 1 Married 4774 23844
## 2806 4 Divorced 3902 5141
## 2807 4 Married 2662 7975
## 2808 2 Married 2856 3692
## 2809 4 Single 1081 16019
## 2810 2 Single 2472 26092
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## 2812 3 Divorced 4197 18624
## 2813 4 Married 9713 24444
## 2814 1 Single 2062 19384
## 2815 1 Married 4284 13588
## 2816 2 Married 4788 25388
## 2817 4 Married 5906 23888
## 2818 4 Single 3886 4223
## 2819 1 Divorced 16823 18991
## 2820 3 Married 2933 14908
## 2821 3 Single 6500 26997
## 2822 4 Divorced 17174 2437
## 2823 1 Married 5033 9364
## 2824 1 Married 2307 14460
## 2825 4 Single 2587 10261
## 2826 2 Married 5507 16822
## 2827 2 Married 4393 26841
## 2828 1 Married 13348 14842
## 2829 4 Divorced 6583 20115
## 2830 4 Married 8103 16495
## 2831 3 Divorced 3978 16031
## 2832 4 Married 2544 7102
## 2833 3 Single 5399 14511
## 2834 3 Single 5487 10410
## 2835 4 Married 6834 19255
## 2836 1 Single 1091 10642
## 2837 3 Married 5736 3987
## 2838 2 Married 2226 6073
## 2839 4 Married 5747 26496
## 2840 3 Single 9854 23352
## 2841 2 Married 5467 2125
## 2842 1 Married 5380 20328
## 2843 1 Married 5151 12315
## 2844 2 Divorced 2133 18115
## 2845 4 Married 17875 11761
## 2846 3 Single 2432 15318
## 2847 4 Divorced 4771 14293
## 2848 4 Married 19161 13738
## 2849 4 Divorced 5087 2900
## 2850 2 Married 2863 19555
## 2851 1 Married 5561 15975
## 2852 3 Single 2144 2122
## 2853 1 Divorced 3065 3995
## 2854 2 Married 2810 9238
## 2855 4 Single 9888 6770
## 2856 3 Divorced 8628 22914
## 2857 1 Single 2867 20006
## 2858 1 Married 5373 6225
## 2859 4 Divorced 6667 16542
## 2860 1 Married 5003 5771
## 2861 4 Divorced 2367 18779
## 2862 1 Single 2858 11473
## 2863 4 Married 5204 13586
## 2864 4 Single 4105 5099
## 2865 4 Single 9679 10138
## 2866 4 Married 5617 21075
## 2867 1 Single 10448 5843
## 2868 3 Married 2897 22474
## 2869 3 Divorced 5968 18079
## 2870 3 Married 7510 16873
## 2871 2 Married 2991 5224
## 2872 2 Married 19636 25811
## 2873 4 Divorced 1129 17536
## 2874 1 Single 13341 25098
## 2875 3 Single 4332 14811
## 2876 3 Married 11031 26862
## 2877 1 Single 4440 25198
## 2878 3 Single 4617 14120
## 2879 4 Single 2647 13672
## 2880 3 Married 6323 26849
## 2881 2 Married 5677 4258
## 2882 2 Married 2187 19655
## 2883 2 Married 3748 4077
## 2884 3 Divorced 3977 7298
## 2885 3 Single 8633 13084
## 2886 3 Divorced 2008 20439
## 2887 2 Married 4440 7636
## 2888 3 Married 3067 6393
## 2889 3 Married 5321 14284
## 2890 1 Divorced 5410 11189
## 2891 4 Married 2782 21412
## 2892 2 Married 11957 17231
## 2893 3 Married 2660 20232
## 2894 3 Single 3375 17624
## 2895 3 Single 5098 18698
## 2896 4 Married 4878 21653
## 2897 2 Single 2837 15919
## 2898 4 Married 2406 4060
## 2899 2 Married 2269 18024
## 2900 4 Single 4108 5340
## 2901 3 Married 13206 3376
## 2902 4 Married 10422 24032
## 2903 4 Married 13744 15471
## 2904 3 Divorced 4907 13684
## 2905 4 Divorced 3482 19788
## 2906 4 Single 2436 13422
## 2907 1 Single 2380 25479
## 2908 4 Single 19431 15302
## 2909 1 Married 1790 26956
## 2910 4 Married 7644 12695
## 2911 2 Divorced 5131 9192
## 2912 3 Divorced 6306 26236
## 2913 4 Married 4787 26124
## 2914 3 Married 18880 17312
## 2915 3 Married 2339 3666
## 2916 2 Married 13570 5640
## 2917 3 Married 6712 8978
## 2918 4 Divorced 5406 10436
## 2919 2 Divorced 8938 12227
## 2920 1 Single 2439 11288
## 2921 4 Single 8837 16642
## 2922 4 Married 5343 5982
## 2923 3 Divorced 6728 14255
## 2924 4 Married 6652 14369
## 2925 3 Single 4850 23333
## 2926 3 Single 2809 2725
## 2927 3 Married 5689 24594
## 2928 3 Married 2001 12549
## 2929 4 Married 2977 8952
## 2930 2 Married 4025 23679
## 2931 1 Single 3785 8489
## 2932 1 Divorced 10854 16586
## 2933 4 Married 12031 8828
## 2934 1 Single 9936 3787
## 2935 3 Single 2966 21378
## 2936 4 Married 2571 12290
## 2937 1 Married 9991 21457
## 2938 2 Married 6142 5174
## 2939 2 Married 5390 13243
## 2940 3 Married 4404 10228
## NumCompaniesWorked OverTime PercentSalaryHike PerformanceRating
## 1 8 Yes 11 3
## 2 1 No 23 4
## 3 6 Yes 15 3
## 4 1 Yes 11 3
## 5 9 No 12 3
## 6 0 No 13 3
## 7 4 Yes 20 4
## 8 1 No 22 4
## 9 0 No 21 4
## 10 6 No 13 3
## 11 0 No 13 3
## 12 0 Yes 12 3
## 13 1 No 17 3
## 14 0 No 11 3
## 15 5 Yes 14 3
## 16 1 No 11 3
## 17 0 Yes 12 3
## 18 1 Yes 13 3
## 19 2 No 16 3
## 20 5 Yes 11 3
## 21 0 No 18 3
## 22 7 No 23 4
## 23 0 No 11 3
## 24 1 No 14 3
## 25 2 No 11 3
## 26 4 No 11 3
## 27 1 Yes 22 4
## 28 0 No 11 3
## 29 3 No 14 3
## 30 3 No 12 3
## 31 4 No 11 3
## 32 2 Yes 13 3
## 33 1 No 13 3
## 34 3 No 14 3
## 35 2 Yes 16 3
## 36 1 No 12 3
## 37 1 Yes 14 3
## 38 1 No 13 3
## 39 9 Yes 14 3
## 40 2 No 19 3
## 41 1 No 12 3
## 42 1 No 13 3
## 43 1 No 12 3
## 44 1 No 15 3
## 45 1 No 23 4
## 46 1 No 12 3
## 47 0 No 20 4
## 48 4 No 21 4
## 49 4 Yes 21 4
## 50 1 No 19 3
## 51 9 Yes 13 3
## 52 1 Yes 13 3
## 53 5 Yes 21 4
## 54 2 Yes 13 3
## 55 7 Yes 19 3
## 56 1 Yes 12 3
## 57 1 No 22 4
## 58 3 Yes 15 3
## 59 3 No 22 4
## 60 1 No 18 3
## 61 1 Yes 22 4
## 62 1 No 11 3
## 63 5 Yes 12 3
## 64 7 No 11 3
## 65 1 No 13 3
## 66 2 Yes 14 3
## 67 1 No 13 3
## 68 2 No 17 3
## 69 4 No 13 3
## 70 0 Yes 17 3
## 71 7 No 11 3
## 72 0 No 23 4
## 73 1 No 17 3
## 74 1 No 17 3
## 75 3 No 12 3
## 76 1 No 23 4
## 77 0 No 14 3
## 78 4 Yes 14 3
## 79 4 No 13 3
## 80 8 Yes 22 4
## 81 1 Yes 12 3
## 82 1 No 18 3
## 83 3 No 14 3
## 84 7 Yes 12 3
## 85 1 No 15 3
## 86 4 No 11 3
## 87 3 No 13 3
## 88 3 No 23 4
## 89 1 No 19 3
## 90 1 No 16 3
## 91 1 No 22 4
## 92 0 Yes 22 4
## 93 1 Yes 12 3
## 94 2 Yes 13 3
## 95 1 No 16 3
## 96 9 No 12 3
## 97 0 No 21 4
## 98 1 No 15 3
## 99 0 No 13 3
## 100 4 No 12 3
## 101 4 Yes 22 4
## 102 1 No 13 3
## 103 1 Yes 18 3
## 104 1 No 14 3
## 105 5 No 14 3
## 106 9 No 21 4
## 107 3 Yes 19 3
## 108 1 Yes 11 3
## 109 1 No 11 3
## 110 1 No 15 3
## 111 3 No 20 4
## 112 1 Yes 24 4
## 113 2 Yes 12 3
## 114 0 No 12 3
## 115 6 No 15 3
## 116 2 No 12 3
## 117 2 No 12 3
## 118 0 No 14 3
## 119 5 No 22 4
## 120 1 Yes 12 3
## 121 1 No 25 4
## 122 0 No 13 3
## 123 9 Yes 18 3
## 124 7 No 13 3
## 125 4 Yes 18 3
## 126 1 No 19 3
## 127 1 No 12 3
## 128 1 Yes 19 3
## 129 0 No 14 3
## 130 1 No 14 3
## 131 4 No 12 3
## 132 4 No 11 3
## 133 3 Yes 11 3
## 134 3 Yes 13 3
## 135 1 No 23 4
## 136 6 No 20 4
## 137 2 No 15 3
## 138 4 No 14 3
## 139 2 No 18 3
## 140 0 Yes 19 3
## 141 7 No 22 4
## 142 5 No 13 3
## 143 3 Yes 20 4
## 144 1 No 14 3
## 145 0 No 17 3
## 146 5 No 14 3
## 147 0 No 13 3
## 148 4 No 13 3
## 149 2 No 21 4
## 150 1 No 14 3
## 151 1 No 11 3
## 152 1 No 13 3
## 153 2 Yes 20 4
## 154 8 No 18 3
## 155 1 No 14 3
## 156 1 No 13 3
## 157 2 No 17 3
## 158 4 Yes 20 4
## 159 7 No 11 3
## 160 6 No 18 3
## 161 1 No 24 4
## 162 6 No 18 3
## 163 1 No 11 3
## 164 3 Yes 16 3
## 165 1 Yes 15 3
## 166 3 No 15 3
## 167 4 No 12 3
## 168 2 No 12 3
## 169 4 No 22 4
## 170 3 No 20 4
## 171 0 Yes 16 3
## 172 0 No 21 4
## 173 4 No 12 3
## 174 1 No 11 3
## 175 4 Yes 11 3
## 176 4 Yes 18 3
## 177 0 No 14 3
## 178 1 No 22 4
## 179 1 No 25 4
## 180 1 No 12 3
## 181 8 Yes 23 4
## 182 2 No 15 3
## 183 1 Yes 22 4
## 184 2 No 15 3
## 185 1 No 11 3
## 186 1 No 13 3
## 187 1 No 14 3
## 188 8 No 11 3
## 189 1 No 17 3
## 190 3 No 18 3
## 191 0 No 14 3
## 192 1 No 16 3
## 193 3 Yes 13 3
## 194 4 No 14 3
## 195 9 No 23 4
## 196 1 Yes 12 3
## 197 5 No 11 3
## 198 2 No 16 3
## 199 9 No 14 3
## 200 3 No 18 3
## 201 5 No 19 3
## 202 1 Yes 15 3
## 203 1 Yes 17 3
## 204 8 Yes 15 3
## 205 7 Yes 19 3
## 206 1 No 22 4
## 207 1 Yes 16 3
## 208 1 No 13 3
## 209 9 No 14 3
## 210 7 No 16 3
## 211 1 No 11 3
## 212 1 No 22 4
## 213 1 No 14 3
## 214 5 No 16 3
## 215 5 Yes 11 3
## 216 3 Yes 18 3
## 217 5 No 15 3
## 218 0 No 14 3
## 219 6 No 12 3
## 220 2 No 14 3
## 221 8 No 16 3
## 222 6 No 21 4
## 223 1 Yes 14 3
## 224 0 No 12 3
## 225 0 No 12 3
## 226 3 No 17 3
## 227 4 No 17 3
## 228 1 No 14 3
## 229 1 No 14 3
## 230 1 Yes 13 3
## 231 7 No 15 3
## 232 1 No 11 3
## 233 8 No 17 3
## 234 3 No 11 3
## 235 5 Yes 13 3
## 236 5 Yes 22 4
## 237 7 No 20 4
## 238 1 Yes 18 3
## 239 2 No 11 3
## 240 0 Yes 14 3
## 241 7 No 14 3
## 242 0 No 18 3
## 243 2 No 16 3
## 244 1 No 17 3
## 245 0 No 11 3
## 246 9 No 12 3
## 247 1 No 13 3
## 248 6 No 13 3
## 249 2 No 14 3
## 250 4 No 17 3
## 251 6 No 11 3
## 252 0 No 25 4
## 253 1 No 19 3
## 254 3 Yes 13 3
## 255 2 No 14 3
## 256 0 No 12 3
## 257 0 Yes 11 3
## 258 0 No 19 3
## 259 1 No 11 3
## 260 0 No 11 3
## 261 1 No 20 4
## 262 3 No 18 3
## 263 4 No 13 3
## 264 3 Yes 12 3
## 265 2 No 11 3
## 266 2 No 19 3
## 267 0 No 21 4
## 268 1 No 12 3
## 269 0 Yes 14 3
## 270 0 No 11 3
## 271 0 Yes 14 3
## 272 1 Yes 12 3
## 273 1 No 23 4
## 274 4 No 14 3
## 275 1 No 17 3
## 276 2 Yes 18 3
## 277 7 No 18 3
## 278 1 Yes 24 4
## 279 1 No 20 4
## 280 3 No 14 3
## 281 3 Yes 16 3
## 282 1 No 25 4
## 283 1 No 18 3
## 284 3 Yes 19 3
## 285 1 Yes 13 3
## 286 1 No 12 3
## 287 3 Yes 22 4
## 288 9 No 14 3
## 289 2 Yes 13 3
## 290 1 No 21 4
## 291 9 Yes 11 3
## 292 4 No 12 3
## 293 1 No 11 3
## 294 1 Yes 12 3
## 295 1 Yes 12 3
## 296 5 No 14 3
## 297 1 No 13 3
## 298 0 No 15 3
## 299 4 No 18 3
## 300 5 No 18 3
## 301 1 No 19 3
## 302 1 No 12 3
## 303 0 No 19 3
## 304 4 No 11 3
## 305 0 No 17 3
## 306 7 No 15 3
## 307 1 No 14 3
## 308 3 No 17 3
## 309 2 Yes 13 3
## 310 0 Yes 12 3
## 311 3 No 12 3
## 312 1 No 18 3
## 313 0 Yes 18 3
## 314 6 No 11 3
## 315 1 Yes 14 3
## 316 0 No 19 3
## 317 7 Yes 12 3
## 318 2 No 15 3
## 319 1 Yes 12 3
## 320 1 Yes 11 3
## 321 1 Yes 11 3
## 322 4 No 12 3
## 323 7 No 16 3
## 324 5 Yes 13 3
## 325 1 No 13 3
## 326 1 No 24 4
## 327 1 No 15 3
## 328 4 Yes 18 3
## 329 3 No 14 3
## 330 4 No 11 3
## 331 1 No 11 3
## 332 0 Yes 13 3
## 333 3 No 12 3
## 334 8 No 16 3
## 335 9 No 14 3
## 336 2 Yes 22 4
## 337 1 Yes 11 3
## 338 0 No 17 3
## 339 1 No 13 3
## 340 1 No 18 3
## 341 7 No 16 3
## 342 0 Yes 14 3
## 343 1 Yes 14 3
## 344 1 Yes 14 3
## 345 0 No 13 3
## 346 1 No 12 3
## 347 6 Yes 15 3
## 348 3 No 19 3
## 349 2 No 14 3
## 350 1 No 14 3
## 351 0 Yes 11 3
## 352 1 No 13 3
## 353 3 No 21 4
## 354 4 Yes 13 3
## 355 7 Yes 12 3
## 356 1 No 17 3
## 357 3 No 23 4
## 358 1 Yes 11 3
## 359 4 No 15 3
## 360 4 No 11 3
## 361 2 No 11 3
## 362 3 Yes 13 3
## 363 1 No 24 4
## 364 1 Yes 13 3
## 365 6 No 20 4
## 366 2 No 14 3
## 367 1 No 18 3
## 368 6 No 15 3
## 369 2 Yes 12 3
## 370 0 Yes 16 3
## 371 1 No 15 3
## 372 9 No 16 3
## 373 9 No 19 3
## 374 1 No 11 3
## 375 1 No 16 3
## 376 8 No 24 4
## 377 4 No 11 3
## 378 4 No 13 3
## 379 8 Yes 13 3
## 380 2 Yes 13 3
## 381 1 Yes 12 3
## 382 1 No 18 3
## 383 0 No 22 4
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## 2940 3 1 2# Convert character variables into Factorlibrary(dummies)#Create dummy variables from the DataFrame . This will transform the data for variables of type factor into o's and 1's for each category
HR_Data_NN_Active_dum = dummy.data.frame(HR_Data_NN_Active[,-2])## Warning in model.matrix.default(~x - 1, model.frame(~x - 1), contrasts =
## FALSE): non-list contrasts argument ignored
## Warning in model.matrix.default(~x - 1, model.frame(~x - 1), contrasts =
## FALSE): non-list contrasts argument ignored
## Warning in model.matrix.default(~x - 1, model.frame(~x - 1), contrasts =
## FALSE): non-list contrasts argument ignored
## Warning in model.matrix.default(~x - 1, model.frame(~x - 1), contrasts =
## FALSE): non-list contrasts argument ignored
## Warning in model.matrix.default(~x - 1, model.frame(~x - 1), contrasts =
## FALSE): non-list contrasts argument ignored
## Warning in model.matrix.default(~x - 1, model.frame(~x - 1), contrasts =
## FALSE): non-list contrasts argument ignored
## Warning in model.matrix.default(~x - 1, model.frame(~x - 1), contrasts =
## FALSE): non-list contrasts argument ignoredHR_Data_NN_Active_dum## Age BusinessTravelNon-Travel BusinessTravelTravel_Frequently
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## 1960 0 4 1
## MaritalStatusMarried MaritalStatusSingle MonthlyIncome MonthlyRate
## 1 0 1 5993 19479
## 2 1 0 5130 24907
## 3 0 1 2090 2396
## 4 1 0 2909 23159
## 5 1 0 3468 16632
## 6 0 1 3068 11864
## 7 1 0 2670 9964
## 8 0 0 2693 13335
## 9 0 1 9526 8787
## 10 1 0 5237 16577
## 11 1 0 2426 16479
## 12 0 1 4193 12682
## 13 0 0 2911 15170
## 14 0 0 2661 8758
## 15 0 1 2028 12947
## 16 0 0 9980 10195
## 17 0 0 3298 15053
## 18 0 0 2935 7324
## 19 1 0 15427 22021
## 20 0 1 3944 4306
## 21 0 0 4011 8232
## 22 0 1 3407 6986
## 23 0 1 11994 21293
## 24 0 1 1232 19281
## 25 0 1 2960 17102
## 26 0 0 19094 10735
## 27 0 1 3919 4681
## 28 1 0 6825 21173
## 29 1 0 10248 2094
## 30 0 1 18947 22822
## 31 0 1 2496 6670
## 32 1 0 6465 19121
## 33 0 1 2206 16117
## 34 1 0 2086 3335
## 35 1 0 2293 3020
## 36 0 0 2645 21923
## 37 1 0 2683 3810
## 38 1 0 2014 9687
## 39 1 0 3419 13072
## 40 1 0 5376 3193
## 41 0 0 1951 10910
## 42 0 0 2341 19715
## 43 0 1 2293 10558
## 44 0 1 8726 2975
## 45 0 1 4011 10781
## 46 1 0 19545 16280
## 47 0 1 4568 10034
## 48 1 0 3022 10227
## 49 0 1 5772 20445
## 50 1 0 2269 4892
## 51 0 1 5381 19294
## 52 0 1 3441 11179
## 53 0 0 5454 4009
## 54 1 0 9884 8302
## 55 1 0 4157 21436
## 56 0 1 13458 15146
## 57 1 0 9069 11031
## 58 1 0 4014 16002
## 59 0 0 5915 9528
## 60 0 0 5993 2689
## 61 1 0 6162 10877
## 62 0 1 2406 5456
## 63 0 0 18740 16701
## 64 0 1 7637 2354
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## 66 0 0 14756 19730
## 67 0 1 6499 22656
## 68 0 0 9724 18787
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## 70 1 0 3388 21777
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## 72 1 0 2703 4956
## 73 0 1 2501 18775
## 74 1 0 6220 7346
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## 79 0 1 13664 25258
## 80 0 0 5021 10425
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## 91 1 0 13503 14115
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## 93 0 0 5209 19760
## 94 1 0 10673 3142
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## 97 1 0 4999 17519
## 98 1 0 4221 8863
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## 103 0 1 2926 19783
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## 122 1 0 6146 15480
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## 125 1 0 6172 20739
## 126 1 0 2368 23300
## 127 1 0 10312 3465
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## 130 1 0 6567 5549
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## 142 0 1 3452 9752
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## 150 0 1 1483 16102
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## 157 1 0 6132 13983
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## 161 1 0 2323 11992
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## 315 1 0 17068 5355
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## 1901 0 1 4230 19225
## 1902 0 1 3780 23428
## 1903 0 0 2768 8416
## 1904 1 0 9071 11563
## 1905 0 0 10648 14394
## 1906 1 0 13610 24619
## 1907 0 0 3408 6705
## 1908 0 1 2983 18398
## 1909 1 0 7632 14295
## 1910 1 0 9824 22908
## 1911 0 0 9950 11533
## 1912 1 0 2093 9260
## 1913 0 1 9980 15318
## 1914 0 1 3894 9129
## 1915 1 0 4051 19658
## 1916 0 1 16835 9873
## 1917 0 1 6230 13430
## 1918 1 0 4717 18659
## 1919 0 1 13237 20364
## 1920 1 0 3755 17872
## 1921 0 1 6582 8346
## 1922 1 0 7406 6950
## 1923 1 0 4805 16177
## 1924 0 0 2741 22808
## 1925 0 0 4262 22645
## 1926 0 0 16184 22578
## 1927 0 0 11557 25291
## 1928 0 1 1878 8059
## 1929 0 0 10932 11373
## 1930 0 1 6811 2112
## 1931 0 0 4306 4267
## 1932 0 1 4859 6698
## 1933 0 1 5337 19921
## 1934 0 1 2340 23213
## 1935 0 1 7491 23848
## 1936 1 0 10527 8984
## 1937 1 0 16595 5626
## 1938 0 0 8834 24666
## 1939 0 0 5577 22087
## 1940 1 0 4707 23914
## 1941 1 0 2400 5530
## 1942 1 0 9824 22174
## 1943 1 0 6447 15701
## 1944 0 0 19502 2125
## 1945 1 0 2725 21630
## 1946 1 0 6272 7428
## 1947 1 0 2127 9100
## 1948 1 0 18200 7999
## 1949 1 0 2096 26376
## 1950 1 0 2886 14168
## 1951 1 0 2033 14470
## 1952 1 0 3622 22794
## 1953 0 0 4233 11512
## 1954 0 1 3681 14004
## 1955 0 0 5460 6219
## 1956 0 0 2187 5013
## 1957 1 0 9602 3010
## 1958 0 1 2836 11757
## 1959 1 0 4089 5718
## 1960 0 0 16627 2671
## NumCompaniesWorked OverTimeNo OverTimeYes PercentSalaryHike
## 1 8 0 1 11
## 2 1 1 0 23
## 3 6 0 1 15
## 4 1 0 1 11
## 5 9 1 0 12
## 6 0 1 0 13
## 7 4 0 1 20
## 8 1 1 0 22
## 9 0 1 0 21
## 10 6 1 0 13
## 11 0 1 0 13
## 12 0 0 1 12
## 13 1 1 0 17
## 14 0 1 0 11
## 15 5 0 1 14
## 16 1 1 0 11
## 17 0 0 1 12
## 18 1 0 1 13
## 19 2 1 0 16
## 20 5 0 1 11
## 21 0 1 0 18
## 22 7 1 0 23
## 23 0 1 0 11
## 24 1 1 0 14
## 25 2 1 0 11
## 26 4 1 0 11
## 27 1 0 1 22
## 28 0 1 0 11
## 29 3 1 0 14
## 30 3 1 0 12
## 31 4 1 0 11
## 32 2 0 1 13
## 33 1 1 0 13
## 34 3 1 0 14
## 35 2 0 1 16
## 36 1 1 0 12
## 37 1 0 1 14
## 38 1 1 0 13
## 39 9 0 1 14
## 40 2 1 0 19
## 41 1 1 0 12
## 42 1 1 0 13
## 43 1 1 0 12
## 44 1 1 0 15
## 45 1 1 0 23
## 46 1 1 0 12
## 47 0 1 0 20
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## 51 9 0 1 13
## 52 1 0 1 13
## 53 5 0 1 21
## 54 2 0 1 13
## 55 7 0 1 19
## 56 1 0 1 12
## 57 1 1 0 22
## 58 3 0 1 15
## 59 3 1 0 22
## 60 1 1 0 18
## 61 1 0 1 22
## 62 1 1 0 11
## 63 5 0 1 12
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## 65 1 1 0 13
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## 74 1 1 0 17
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## TotalWorkingYears TrainingTimesLastYear WorkLifeBalance
## 1 8 0 1
## 2 10 3 3
## 3 7 3 3
## 4 8 3 3
## 5 6 3 3
## 6 8 2 2
## 7 12 3 2
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## 20 6 3 3
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## 22 10 4 3
## 23 13 4 3
## 24 0 6 3
## 25 8 2 3
## 26 26 3 2
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## [ reached 'max' / getOption("max.print") -- omitted 980 rows ]dim(HR_Data_NN_Active_dum)## [1] 2940 51names(HR_Data_NN_Active_dum)## [1] "Age" "BusinessTravelNon-Travel"
## [3] "BusinessTravelTravel_Frequently" "BusinessTravelTravel_Rarely"
## [5] "DailyRate" "DepartmentHuman Resources"
## [7] "DepartmentResearch & Development" "DepartmentSales"
## [9] "DistanceFromHome" "Education"
## [11] "EducationFieldHuman Resources" "EducationFieldLife Sciences"
## [13] "EducationFieldMarketing" "EducationFieldMedical"
## [15] "EducationFieldOther" "EducationFieldTechnical Degree"
## [17] "EnvironmentSatisfaction" "GenderFemale"
## [19] "GenderMale" "HourlyRate"
## [21] "JobInvolvement" "JobLevel"
## [23] "JobRoleHealthcare Representative" "JobRoleHuman Resources"
## [25] "JobRoleLaboratory Technician" "JobRoleManager"
## [27] "JobRoleManufacturing Director" "JobRoleResearch Director"
## [29] "JobRoleResearch Scientist" "JobRoleSales Executive"
## [31] "JobRoleSales Representative" "JobSatisfaction"
## [33] "MaritalStatusDivorced" "MaritalStatusMarried"
## [35] "MaritalStatusSingle" "MonthlyIncome"
## [37] "MonthlyRate" "NumCompaniesWorked"
## [39] "OverTimeNo" "OverTimeYes"
## [41] "PercentSalaryHike" "PerformanceRating"
## [43] "RelationshipSatisfaction" "StockOptionLevel"
## [45] "TotalWorkingYears" "TrainingTimesLastYear"
## [47] "WorkLifeBalance" "YearsAtCompany"
## [49] "YearsInCurrentRole" "YearsSinceLastPromotion"
## [51] "YearsWithCurrManager"# Scale the Data in new dataframe tranformed using Dummy variables for Character variables and numeric variables
HR_Data_NN_Active_scale <- scale(HR_Data_NN_Active_dum)
HR_Data_NN_Active_scale = as.data.table(HR_Data_NN_Active_scale)
head(HR_Data_NN_Active_scale)## Age BusinessTravelNon-Travel BusinessTravelTravel_Frequently
## 1: 0.446274487 -0.3370426 -0.4817767
## 2: 1.322140303 -0.3370426 2.0749444
## 3: 0.008341579 -0.3370426 -0.4817767
## 4: -0.429591329 -0.3370426 2.0749444
## 5: -1.086490691 -0.3370426 -0.4817767
## 6: -0.539074556 -0.3370426 2.0749444
## BusinessTravelTravel_Rarely DailyRate DepartmentHuman Resources
## 1: 0.6397318 0.7424002 -0.2115677
## 2: -1.5626235 -1.2975539 -0.2115677
## 3: 0.6397318 1.4141227 -0.2115677
## 4: -1.5626235 1.4612176 -0.2115677
## 5: 0.6397318 -0.5242055 -0.2115677
## 6: -1.5626235 0.5019682 -0.2115677
## DepartmentResearch & Development DepartmentSales DistanceFromHome
## 1: -1.3738171 1.5149862 -1.0107374
## 2: 0.7276513 -0.6598475 -0.1471247
## 3: 0.7276513 -0.6598475 -0.8873642
## 4: 0.7276513 -0.6598475 -0.7639909
## 5: 0.7276513 -0.6598475 -0.8873642
## 6: 0.7276513 -0.6598475 -0.8873642
## Education EducationFieldHuman Resources EducationFieldLife Sciences
## 1: -0.8915366 -0.136765 1.1938415
## 2: -1.8681080 -0.136765 1.1938415
## 3: -0.8915366 -0.136765 -0.8373472
## 4: 1.0616062 -0.136765 1.1938415
## 5: -1.8681080 -0.136765 -0.8373472
## 6: -0.8915366 -0.136765 1.1938415
## EducationFieldMarketing EducationFieldMedical EducationFieldOther
## 1: -0.3481957 -0.6790256 -0.2430179
## 2: -0.3481957 -0.6790256 -0.2430179
## 3: -0.3481957 -0.6790256 4.1135230
## 4: -0.3481957 -0.6790256 -0.2430179
## 5: -0.3481957 1.4721977 -0.2430179
## 6: -0.3481957 -0.6790256 -0.2430179
## EducationFieldTechnical Degree EnvironmentSatisfaction GenderFemale
## 1: -0.31404 -0.6604183 1.2245366
## 2: -0.31404 0.2545816 -0.8163577
## 3: -0.31404 1.1695816 -0.8163577
## 4: -0.31404 1.1695816 1.2245366
## 5: -0.31404 -1.5754183 -0.8163577
## 6: -0.31404 1.1695816 -0.8163577
## GenderMale HourlyRate JobInvolvement JobLevel
## 1: -1.2245366 1.3829030 0.3796076 -0.05777772
## 2: 0.8163577 -0.2406358 -1.0259922 -0.05777772
## 3: 0.8163577 1.2845067 -1.0259922 -0.96132286
## 4: -1.2245366 -0.4866266 0.3796076 -0.96132286
## 5: 0.8163577 -1.2737969 0.3796076 -0.96132286
## 6: 0.8163577 0.6449308 0.3796076 -0.96132286
## JobRoleHealthcare Representative JobRoleHuman Resources
## 1: -0.3127314 -0.1914651
## 2: -0.3127314 -0.1914651
## 3: -0.3127314 -0.1914651
## 4: -0.3127314 -0.1914651
## 5: -0.3127314 -0.1914651
## 6: -0.3127314 -0.1914651
## JobRoleLaboratory Technician JobRoleManager
## 1: -0.4623852 -0.2730129
## 2: -0.4623852 -0.2730129
## 3: 2.1619633 -0.2730129
## 4: -0.4623852 -0.2730129
## 5: 2.1619633 -0.2730129
## 6: 2.1619633 -0.2730129
## JobRoleManufacturing Director JobRoleResearch Director
## 1: -0.3307518 -0.2398633
## 2: -0.3307518 -0.2398633
## 3: -0.3307518 -0.2398633
## 4: -0.3307518 -0.2398633
## 5: -0.3307518 -0.2398633
## 6: -0.3307518 -0.2398633
## JobRoleResearch Scientist JobRoleSales Executive
## 1: -0.4977886 1.8729679
## 2: 2.0082018 -0.5337304
## 3: -0.4977886 -0.5337304
## 4: 2.0082018 -0.5337304
## 5: -0.4977886 -0.5337304
## 6: -0.4977886 -0.5337304
## JobRoleSales Representative JobSatisfaction MaritalStatusDivorced
## 1: -0.2445834 1.1530574 -0.5347821
## 2: -0.2445834 -0.6607408 -0.5347821
## 3: -0.2445834 0.2461583 -0.5347821
## 4: -0.2445834 0.2461583 -0.5347821
## 5: -0.2445834 -0.6607408 -0.5347821
## 6: -0.2445834 1.1530574 -0.5347821
## MaritalStatusMarried MaritalStatusSingle MonthlyIncome MonthlyRate
## 1: -0.9187651 1.4584018 -0.1083311 0.7258965
## 2: 1.0880473 -0.6854489 -0.2916690 1.4886229
## 3: -0.9187651 1.4584018 -0.9374942 -1.6745560
## 4: 1.0880473 -0.6854489 -0.7635038 1.2429991
## 5: 1.0880473 -0.6854489 -0.6447484 0.3258445
## 6: -0.9187651 1.4584018 -0.7297254 -0.3441407
## NumCompaniesWorked OverTimeNo OverTimeYes PercentSalaryHike
## 1: 2.1247745 -1.5914748 1.5914748 -1.15035841
## 2: -0.6779341 0.6281343 -0.6281343 2.12894385
## 3: 1.3240006 -1.5914748 1.5914748 -0.05725766
## 4: -0.6779341 -1.5914748 1.5914748 -1.15035841
## 5: 2.5251614 0.6281343 -0.6281343 -0.87708322
## 6: -1.0783210 0.6281343 -0.6281343 -0.60380804
## PerformanceRating RelationshipSatisfaction StockOptionLevel
## 1: -0.4261575 -1.5839088 -0.9318559
## 2: 2.3457520 1.1912353 0.2419472
## 3: -0.4261575 -0.6588608 -0.9318559
## 4: -0.4261575 0.2661873 -0.9318559
## 5: -0.4261575 1.1912353 0.2419472
## 6: -0.4261575 0.2661873 -0.9318559
## TotalWorkingYears TrainingTimesLastYear WorkLifeBalance YearsAtCompany
## 1: -0.4215707 -2.1716124 -2.4933963 -0.164585111
## 2: -0.1644834 0.1556806 0.3380387 0.488424642
## 3: -0.5501144 0.1556806 0.3380387 -1.144099741
## 4: -0.4215707 0.1556806 0.3380387 0.161919765
## 5: -0.6786581 0.1556806 0.3380387 -0.817594864
## 6: -0.4215707 -0.6200837 -1.0776788 -0.001332673
## YearsInCurrentRole YearsSinceLastPromotion YearsWithCurrManager
## 1: -0.06328513 -0.67903017 0.2457922
## 2: 0.76486751 -0.36865258 0.8064043
## 3: -1.16748866 -0.67903017 -1.1557381
## 4: 0.76486751 0.25210262 -1.1557381
## 5: -0.61538690 -0.05827498 -0.5951260
## 6: 0.76486751 0.25210262 0.5260982names(HR_Data_NN_Active_scale)## [1] "Age" "BusinessTravelNon-Travel"
## [3] "BusinessTravelTravel_Frequently" "BusinessTravelTravel_Rarely"
## [5] "DailyRate" "DepartmentHuman Resources"
## [7] "DepartmentResearch & Development" "DepartmentSales"
## [9] "DistanceFromHome" "Education"
## [11] "EducationFieldHuman Resources" "EducationFieldLife Sciences"
## [13] "EducationFieldMarketing" "EducationFieldMedical"
## [15] "EducationFieldOther" "EducationFieldTechnical Degree"
## [17] "EnvironmentSatisfaction" "GenderFemale"
## [19] "GenderMale" "HourlyRate"
## [21] "JobInvolvement" "JobLevel"
## [23] "JobRoleHealthcare Representative" "JobRoleHuman Resources"
## [25] "JobRoleLaboratory Technician" "JobRoleManager"
## [27] "JobRoleManufacturing Director" "JobRoleResearch Director"
## [29] "JobRoleResearch Scientist" "JobRoleSales Executive"
## [31] "JobRoleSales Representative" "JobSatisfaction"
## [33] "MaritalStatusDivorced" "MaritalStatusMarried"
## [35] "MaritalStatusSingle" "MonthlyIncome"
## [37] "MonthlyRate" "NumCompaniesWorked"
## [39] "OverTimeNo" "OverTimeYes"
## [41] "PercentSalaryHike" "PerformanceRating"
## [43] "RelationshipSatisfaction" "StockOptionLevel"
## [45] "TotalWorkingYears" "TrainingTimesLastYear"
## [47] "WorkLifeBalance" "YearsAtCompany"
## [49] "YearsInCurrentRole" "YearsSinceLastPromotion"
## [51] "YearsWithCurrManager"dim(HR_Data_NN_Active_scale)## [1] 2940 51# Combine the active Dataset which is scaled and the new dataset with Traget columns
Target_NN <- HR_Data_NN_Active$Attrition
HR_Data_NN_Active_New <- cbind(Target_NN , HR_Data_NN_Active_scale)
dim(HR_Data_NN_Active_New)## [1] 2940 52names(HR_Data_NN_Active_New)## [1] "Target_NN" "Age"
## [3] "BusinessTravelNon-Travel" "BusinessTravelTravel_Frequently"
## [5] "BusinessTravelTravel_Rarely" "DailyRate"
## [7] "DepartmentHuman Resources" "DepartmentResearch & Development"
## [9] "DepartmentSales" "DistanceFromHome"
## [11] "Education" "EducationFieldHuman Resources"
## [13] "EducationFieldLife Sciences" "EducationFieldMarketing"
## [15] "EducationFieldMedical" "EducationFieldOther"
## [17] "EducationFieldTechnical Degree" "EnvironmentSatisfaction"
## [19] "GenderFemale" "GenderMale"
## [21] "HourlyRate" "JobInvolvement"
## [23] "JobLevel" "JobRoleHealthcare Representative"
## [25] "JobRoleHuman Resources" "JobRoleLaboratory Technician"
## [27] "JobRoleManager" "JobRoleManufacturing Director"
## [29] "JobRoleResearch Director" "JobRoleResearch Scientist"
## [31] "JobRoleSales Executive" "JobRoleSales Representative"
## [33] "JobSatisfaction" "MaritalStatusDivorced"
## [35] "MaritalStatusMarried" "MaritalStatusSingle"
## [37] "MonthlyIncome" "MonthlyRate"
## [39] "NumCompaniesWorked" "OverTimeNo"
## [41] "OverTimeYes" "PercentSalaryHike"
## [43] "PerformanceRating" "RelationshipSatisfaction"
## [45] "StockOptionLevel" "TotalWorkingYears"
## [47] "TrainingTimesLastYear" "WorkLifeBalance"
## [49] "YearsAtCompany" "YearsInCurrentRole"
## [51] "YearsSinceLastPromotion" "YearsWithCurrManager"class(HR_Data_NN_Active_New)## [1] "data.table" "data.frame"setnames(x=HR_Data_NN_Active_New, old=names(HR_Data_NN_Active_New), new=gsub(" ","",names(HR_Data_NN_Active_New)))
setnames(x=HR_Data_NN_Active_New, old=names(HR_Data_NN_Active_New), new=gsub("&","",names(HR_Data_NN_Active_New)))
names(HR_Data_NN_Active_New)## [1] "Target_NN" "Age"
## [3] "BusinessTravelNon-Travel" "BusinessTravelTravel_Frequently"
## [5] "BusinessTravelTravel_Rarely" "DailyRate"
## [7] "DepartmentHumanResources" "DepartmentResearchDevelopment"
## [9] "DepartmentSales" "DistanceFromHome"
## [11] "Education" "EducationFieldHumanResources"
## [13] "EducationFieldLifeSciences" "EducationFieldMarketing"
## [15] "EducationFieldMedical" "EducationFieldOther"
## [17] "EducationFieldTechnicalDegree" "EnvironmentSatisfaction"
## [19] "GenderFemale" "GenderMale"
## [21] "HourlyRate" "JobInvolvement"
## [23] "JobLevel" "JobRoleHealthcareRepresentative"
## [25] "JobRoleHumanResources" "JobRoleLaboratoryTechnician"
## [27] "JobRoleManager" "JobRoleManufacturingDirector"
## [29] "JobRoleResearchDirector" "JobRoleResearchScientist"
## [31] "JobRoleSalesExecutive" "JobRoleSalesRepresentative"
## [33] "JobSatisfaction" "MaritalStatusDivorced"
## [35] "MaritalStatusMarried" "MaritalStatusSingle"
## [37] "MonthlyIncome" "MonthlyRate"
## [39] "NumCompaniesWorked" "OverTimeNo"
## [41] "OverTimeYes" "PercentSalaryHike"
## [43] "PerformanceRating" "RelationshipSatisfaction"
## [45] "StockOptionLevel" "TotalWorkingYears"
## [47] "TrainingTimesLastYear" "WorkLifeBalance"
## [49] "YearsAtCompany" "YearsInCurrentRole"
## [51] "YearsSinceLastPromotion" "YearsWithCurrManager"str(HR_Data_NN_Active_New)## Classes 'data.table' and 'data.frame': 2940 obs. of 52 variables:
## $ Target_NN : num 1 0 1 0 0 0 0 0 0 0 ...
## $ Age : num 0.44627 1.32214 0.00834 -0.42959 -1.08649 ...
## $ BusinessTravelNon-Travel : num -0.337 -0.337 -0.337 -0.337 -0.337 ...
## $ BusinessTravelTravel_Frequently: num -0.482 2.075 -0.482 2.075 -0.482 ...
## $ BusinessTravelTravel_Rarely : num 0.64 -1.56 0.64 -1.56 0.64 ...
## $ DailyRate : num 0.742 -1.298 1.414 1.461 -0.524 ...
## $ DepartmentHumanResources : num -0.212 -0.212 -0.212 -0.212 -0.212 ...
## $ DepartmentResearchDevelopment : num -1.374 0.728 0.728 0.728 0.728 ...
## $ DepartmentSales : num 1.51 -0.66 -0.66 -0.66 -0.66 ...
## $ DistanceFromHome : num -1.011 -0.147 -0.887 -0.764 -0.887 ...
## $ Education : num -0.892 -1.868 -0.892 1.062 -1.868 ...
## $ EducationFieldHumanResources : num -0.137 -0.137 -0.137 -0.137 -0.137 ...
## $ EducationFieldLifeSciences : num 1.194 1.194 -0.837 1.194 -0.837 ...
## $ EducationFieldMarketing : num -0.348 -0.348 -0.348 -0.348 -0.348 ...
## $ EducationFieldMedical : num -0.679 -0.679 -0.679 -0.679 1.472 ...
## $ EducationFieldOther : num -0.243 -0.243 4.114 -0.243 -0.243 ...
## $ EducationFieldTechnicalDegree : num -0.314 -0.314 -0.314 -0.314 -0.314 ...
## $ EnvironmentSatisfaction : num -0.66 0.255 1.17 1.17 -1.575 ...
## $ GenderFemale : num 1.225 -0.816 -0.816 1.225 -0.816 ...
## $ GenderMale : num -1.225 0.816 0.816 -1.225 0.816 ...
## $ HourlyRate : num 1.383 -0.241 1.285 -0.487 -1.274 ...
## $ JobInvolvement : num 0.38 -1.03 -1.03 0.38 0.38 ...
## $ JobLevel : num -0.0578 -0.0578 -0.9613 -0.9613 -0.9613 ...
## $ JobRoleHealthcareRepresentative: num -0.313 -0.313 -0.313 -0.313 -0.313 ...
## $ JobRoleHumanResources : num -0.191 -0.191 -0.191 -0.191 -0.191 ...
## $ JobRoleLaboratoryTechnician : num -0.462 -0.462 2.162 -0.462 2.162 ...
## $ JobRoleManager : num -0.273 -0.273 -0.273 -0.273 -0.273 ...
## $ JobRoleManufacturingDirector : num -0.331 -0.331 -0.331 -0.331 -0.331 ...
## $ JobRoleResearchDirector : num -0.24 -0.24 -0.24 -0.24 -0.24 ...
## $ JobRoleResearchScientist : num -0.498 2.008 -0.498 2.008 -0.498 ...
## $ JobRoleSalesExecutive : num 1.873 -0.534 -0.534 -0.534 -0.534 ...
## $ JobRoleSalesRepresentative : num -0.245 -0.245 -0.245 -0.245 -0.245 ...
## $ JobSatisfaction : num 1.153 -0.661 0.246 0.246 -0.661 ...
## $ MaritalStatusDivorced : num -0.535 -0.535 -0.535 -0.535 -0.535 ...
## $ MaritalStatusMarried : num -0.919 1.088 -0.919 1.088 1.088 ...
## $ MaritalStatusSingle : num 1.458 -0.685 1.458 -0.685 -0.685 ...
## $ MonthlyIncome : num -0.108 -0.292 -0.937 -0.764 -0.645 ...
## $ MonthlyRate : num 0.726 1.489 -1.675 1.243 0.326 ...
## $ NumCompaniesWorked : num 2.125 -0.678 1.324 -0.678 2.525 ...
## $ OverTimeNo : num -1.591 0.628 -1.591 -1.591 0.628 ...
## $ OverTimeYes : num 1.591 -0.628 1.591 1.591 -0.628 ...
## $ PercentSalaryHike : num -1.1504 2.1289 -0.0573 -1.1504 -0.8771 ...
## $ PerformanceRating : num -0.426 2.346 -0.426 -0.426 -0.426 ...
## $ RelationshipSatisfaction : num -1.584 1.191 -0.659 0.266 1.191 ...
## $ StockOptionLevel : num -0.932 0.242 -0.932 -0.932 0.242 ...
## $ TotalWorkingYears : num -0.422 -0.164 -0.55 -0.422 -0.679 ...
## $ TrainingTimesLastYear : num -2.172 0.156 0.156 0.156 0.156 ...
## $ WorkLifeBalance : num -2.493 0.338 0.338 0.338 0.338 ...
## $ YearsAtCompany : num -0.165 0.488 -1.144 0.162 -0.818 ...
## $ YearsInCurrentRole : num -0.0633 0.7649 -1.1675 0.7649 -0.6154 ...
## $ YearsSinceLastPromotion : num -0.679 -0.3687 -0.679 0.2521 -0.0583 ...
## $ YearsWithCurrManager : num 0.246 0.806 -1.156 -1.156 -0.595 ...
## - attr(*, ".internal.selfref")=<externalptr>HR_Data_NN_Active_New <- as.data.frame(HR_Data_NN_Active_New)
class(HR_Data_NN_Active_New)## [1] "data.frame"# HR_Data_NN_Active_New## split into training and test sets
set.seed(123)
prop.table(table(HR_Data_NN$Attrition))##
## No Yes
## 0.8387755 0.1612245split = sample.split(HR_Data_NN_Active_New$Target, SplitRatio = 0.7)
traindata_NN = subset(HR_Data_NN_Active_New, split == TRUE)
testdata_NN = subset(HR_Data_NN_Active_New, split == FALSE)
prop.table(table(traindata_NN$Target_NN))##
## 0 1
## 0.8386783 0.1613217prop.table(table(testdata_NN$Target_NN))##
## 0 1
## 0.8390023 0.1609977names(traindata_NN)## [1] "Target_NN" "Age"
## [3] "BusinessTravelNon-Travel" "BusinessTravelTravel_Frequently"
## [5] "BusinessTravelTravel_Rarely" "DailyRate"
## [7] "DepartmentHumanResources" "DepartmentResearchDevelopment"
## [9] "DepartmentSales" "DistanceFromHome"
## [11] "Education" "EducationFieldHumanResources"
## [13] "EducationFieldLifeSciences" "EducationFieldMarketing"
## [15] "EducationFieldMedical" "EducationFieldOther"
## [17] "EducationFieldTechnicalDegree" "EnvironmentSatisfaction"
## [19] "GenderFemale" "GenderMale"
## [21] "HourlyRate" "JobInvolvement"
## [23] "JobLevel" "JobRoleHealthcareRepresentative"
## [25] "JobRoleHumanResources" "JobRoleLaboratoryTechnician"
## [27] "JobRoleManager" "JobRoleManufacturingDirector"
## [29] "JobRoleResearchDirector" "JobRoleResearchScientist"
## [31] "JobRoleSalesExecutive" "JobRoleSalesRepresentative"
## [33] "JobSatisfaction" "MaritalStatusDivorced"
## [35] "MaritalStatusMarried" "MaritalStatusSingle"
## [37] "MonthlyIncome" "MonthlyRate"
## [39] "NumCompaniesWorked" "OverTimeNo"
## [41] "OverTimeYes" "PercentSalaryHike"
## [43] "PerformanceRating" "RelationshipSatisfaction"
## [45] "StockOptionLevel" "TotalWorkingYears"
## [47] "TrainingTimesLastYear" "WorkLifeBalance"
## [49] "YearsAtCompany" "YearsInCurrentRole"
## [51] "YearsSinceLastPromotion" "YearsWithCurrManager"str(HR_Data_NN_Active_New)## 'data.frame': 2940 obs. of 52 variables:
## $ Target_NN : num 1 0 1 0 0 0 0 0 0 0 ...
## $ Age : num 0.44627 1.32214 0.00834 -0.42959 -1.08649 ...
## $ BusinessTravelNon-Travel : num -0.337 -0.337 -0.337 -0.337 -0.337 ...
## $ BusinessTravelTravel_Frequently: num -0.482 2.075 -0.482 2.075 -0.482 ...
## $ BusinessTravelTravel_Rarely : num 0.64 -1.56 0.64 -1.56 0.64 ...
## $ DailyRate : num 0.742 -1.298 1.414 1.461 -0.524 ...
## $ DepartmentHumanResources : num -0.212 -0.212 -0.212 -0.212 -0.212 ...
## $ DepartmentResearchDevelopment : num -1.374 0.728 0.728 0.728 0.728 ...
## $ DepartmentSales : num 1.51 -0.66 -0.66 -0.66 -0.66 ...
## $ DistanceFromHome : num -1.011 -0.147 -0.887 -0.764 -0.887 ...
## $ Education : num -0.892 -1.868 -0.892 1.062 -1.868 ...
## $ EducationFieldHumanResources : num -0.137 -0.137 -0.137 -0.137 -0.137 ...
## $ EducationFieldLifeSciences : num 1.194 1.194 -0.837 1.194 -0.837 ...
## $ EducationFieldMarketing : num -0.348 -0.348 -0.348 -0.348 -0.348 ...
## $ EducationFieldMedical : num -0.679 -0.679 -0.679 -0.679 1.472 ...
## $ EducationFieldOther : num -0.243 -0.243 4.114 -0.243 -0.243 ...
## $ EducationFieldTechnicalDegree : num -0.314 -0.314 -0.314 -0.314 -0.314 ...
## $ EnvironmentSatisfaction : num -0.66 0.255 1.17 1.17 -1.575 ...
## $ GenderFemale : num 1.225 -0.816 -0.816 1.225 -0.816 ...
## $ GenderMale : num -1.225 0.816 0.816 -1.225 0.816 ...
## $ HourlyRate : num 1.383 -0.241 1.285 -0.487 -1.274 ...
## $ JobInvolvement : num 0.38 -1.03 -1.03 0.38 0.38 ...
## $ JobLevel : num -0.0578 -0.0578 -0.9613 -0.9613 -0.9613 ...
## $ JobRoleHealthcareRepresentative: num -0.313 -0.313 -0.313 -0.313 -0.313 ...
## $ JobRoleHumanResources : num -0.191 -0.191 -0.191 -0.191 -0.191 ...
## $ JobRoleLaboratoryTechnician : num -0.462 -0.462 2.162 -0.462 2.162 ...
## $ JobRoleManager : num -0.273 -0.273 -0.273 -0.273 -0.273 ...
## $ JobRoleManufacturingDirector : num -0.331 -0.331 -0.331 -0.331 -0.331 ...
## $ JobRoleResearchDirector : num -0.24 -0.24 -0.24 -0.24 -0.24 ...
## $ JobRoleResearchScientist : num -0.498 2.008 -0.498 2.008 -0.498 ...
## $ JobRoleSalesExecutive : num 1.873 -0.534 -0.534 -0.534 -0.534 ...
## $ JobRoleSalesRepresentative : num -0.245 -0.245 -0.245 -0.245 -0.245 ...
## $ JobSatisfaction : num 1.153 -0.661 0.246 0.246 -0.661 ...
## $ MaritalStatusDivorced : num -0.535 -0.535 -0.535 -0.535 -0.535 ...
## $ MaritalStatusMarried : num -0.919 1.088 -0.919 1.088 1.088 ...
## $ MaritalStatusSingle : num 1.458 -0.685 1.458 -0.685 -0.685 ...
## $ MonthlyIncome : num -0.108 -0.292 -0.937 -0.764 -0.645 ...
## $ MonthlyRate : num 0.726 1.489 -1.675 1.243 0.326 ...
## $ NumCompaniesWorked : num 2.125 -0.678 1.324 -0.678 2.525 ...
## $ OverTimeNo : num -1.591 0.628 -1.591 -1.591 0.628 ...
## $ OverTimeYes : num 1.591 -0.628 1.591 1.591 -0.628 ...
## $ PercentSalaryHike : num -1.1504 2.1289 -0.0573 -1.1504 -0.8771 ...
## $ PerformanceRating : num -0.426 2.346 -0.426 -0.426 -0.426 ...
## $ RelationshipSatisfaction : num -1.584 1.191 -0.659 0.266 1.191 ...
## $ StockOptionLevel : num -0.932 0.242 -0.932 -0.932 0.242 ...
## $ TotalWorkingYears : num -0.422 -0.164 -0.55 -0.422 -0.679 ...
## $ TrainingTimesLastYear : num -2.172 0.156 0.156 0.156 0.156 ...
## $ WorkLifeBalance : num -2.493 0.338 0.338 0.338 0.338 ...
## $ YearsAtCompany : num -0.165 0.488 -1.144 0.162 -0.818 ...
## $ YearsInCurrentRole : num -0.0633 0.7649 -1.1675 0.7649 -0.6154 ...
## $ YearsSinceLastPromotion : num -0.679 -0.3687 -0.679 0.2521 -0.0583 ...
## $ YearsWithCurrManager : num 0.246 0.806 -1.156 -1.156 -0.595 ...# Formula for NN
form_HR_NN <- as.formula(`Target_NN` ~ `BusinessTravelTravel_Rarely` + `DepartmentHumanResources` + `DepartmentSales` + `Education` + `EducationFieldLifeSciences` + `EducationFieldMedical`+ `EducationFieldTechnicalDegree` + `GenderFemale` + `HourlyRate` + `JobLevel` + `JobRoleHumanResources` + `JobRoleManager` + `JobRoleResearchDirector` + `JobRoleSalesExecutive` + `JobSatisfaction` + `MaritalStatusMarried` + `MonthlyIncome` + `NumCompaniesWorked` + `OverTimeYes` + `PerformanceRating` + `StockOptionLevel` + `TrainingTimesLastYear` + `YearsAtCompany` + `YearsSinceLastPromotion` + `Age` + `BusinessTravelTravel_Frequently` + `DailyRate` + `DepartmentResearchDevelopment` + `DistanceFromHome` + `EducationFieldHumanResources` + `EducationFieldMarketing` + `EducationFieldOther` + `EnvironmentSatisfaction` + `GenderMale`+ `JobInvolvement` + `JobRoleHealthcareRepresentative` + `JobRoleLaboratoryTechnician` + `JobRoleManufacturingDirector` + `JobRoleResearchScientist` + `JobRoleSalesRepresentative` + `MaritalStatusDivorced` + `MaritalStatusSingle` + `MonthlyRate` + `OverTimeNo` + `PercentSalaryHike` + `RelationshipSatisfaction` + `TotalWorkingYears` + `WorkLifeBalance` + `YearsInCurrentRole` + `YearsWithCurrManager` )
#`Target_NN` ~ `BusinessTravelNon-Travel` + `BusinessTravelTravel_Rarely` + `DepartmentHumanResources` + `DepartmentSales` + `Education` + `EducationFieldLifeSciences` + `EducationFieldMedical` + `EducationFieldTechnicalDegree` + `GenderFemale` + `HourlyRate` + `JobLevel` + `JobRoleHumanResources` + `JobRoleManager` + `JobRoleResearchDirector` + `JobRoleSalesExecutive` + `JobSatisfaction` + `MaritalStatusMarried` + `MonthlyIncome` + `NumCompaniesWorked` + `OverTimeYes` + `PerformanceRating` + `StockOptionLevel` + `TrainingTimesLastYear` + `YearsAtCompany` + `YearsSinceLastPromotion` + `Age` + `BusinessTravelTravel_Frequently` + `DailyRate` + `DepartmentResearch&Development` + `DistanceFromHome` + `EducationFieldHumanResources` + `EducationFieldMarketing` + `EducationFieldOther` + `EnvironmentSatisfaction` + `GenderMale` + `JobInvolvement` + `JobRoleHealthcareRepresentative` + `JobRoleLaboratoryTechnician` + `JobRoleManufacturingDirector` + `JobRoleResearchScientist` + `JobRoleSalesRepresentative` + `MaritalStatusDivorced` + `MaritalStatusSingle` + `MonthlyRate` + `OverTimeNo` + `PercentSalaryHike` + `RelationshipSatisfaction` + `TotalWorkingYears` + `WorkLifeBalance` + `YearsInCurrentRole` + `YearsWithCurrManager`
print(form_HR_NN)## Target_NN ~ BusinessTravelTravel_Rarely + DepartmentHumanResources +
## DepartmentSales + Education + EducationFieldLifeSciences +
## EducationFieldMedical + EducationFieldTechnicalDegree + GenderFemale +
## HourlyRate + JobLevel + JobRoleHumanResources + JobRoleManager +
## JobRoleResearchDirector + JobRoleSalesExecutive + JobSatisfaction +
## MaritalStatusMarried + MonthlyIncome + NumCompaniesWorked +
## OverTimeYes + PerformanceRating + StockOptionLevel + TrainingTimesLastYear +
## YearsAtCompany + YearsSinceLastPromotion + Age + BusinessTravelTravel_Frequently +
## DailyRate + DepartmentResearchDevelopment + DistanceFromHome +
## EducationFieldHumanResources + EducationFieldMarketing +
## EducationFieldOther + EnvironmentSatisfaction + GenderMale +
## JobInvolvement + JobRoleHealthcareRepresentative + JobRoleLaboratoryTechnician +
## JobRoleManufacturingDirector + JobRoleResearchScientist +
## JobRoleSalesRepresentative + MaritalStatusDivorced + MaritalStatusSingle +
## MonthlyRate + OverTimeNo + PercentSalaryHike + RelationshipSatisfaction +
## TotalWorkingYears + WorkLifeBalance + YearsInCurrentRole +
## YearsWithCurrManager# Build the model
?neuralnet## starting httpd help server ... doneNN_HR =neuralnet(formula = form_HR_NN ,data = traindata_NN, hidden=10, threshold=0.1,lifesign = "full" )## hidden: 10 thresh: 0.1 rep: 1/1 steps: 1000 min thresh: 0.493834929817948
## 2000 min thresh: 0.180585167851248
## 3000 min thresh: 0.10020125792683
## 3106 error: 36.18175 time: 13.82 secsplot(NN_HR, cex=0.6, lwd=0.1)#print(NN_HR$net.result)
summary(NN_HR$net.result)## Length Class Mode
## [1,] 2058 -none- numericNetwork model after changing the threshold
library(neuralnet)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 2190963 117.1 4042962 216.0 4042962 216.0
## Vcells 5307090 40.5 10148388 77.5 10148388 77.5NN_HR2 <- neuralnet(formula = form_HR_NN,
data = traindata_NN,
hidden = c(4,4),
linear.output = FALSE,
lifesign = "full",
lifesign.step = 1,
threshold = 0.7,
stepmax = 2000)## hidden: 4, 4 thresh: 0.7 rep: 1/1 steps: 1 min thresh: 14.1595548628804
## 2 min thresh: 6.56517047106904
## 3 min thresh: 6.56517047106904
## 4 min thresh: 4.14952154414646
## 5 min thresh: 3.69608194801031
## 6 min thresh: 3.69608194801031
## 7 min thresh: 3.69608194801031
## 8 min thresh: 3.69608194801031
## 9 min thresh: 3.69608194801031
## 10 min thresh: 3.69608194801031
## 11 min thresh: 3.69608194801031
## 12 min thresh: 3.69608194801031
## 13 min thresh: 3.69608194801031
## 14 min thresh: 3.69608194801031
## 15 min thresh: 3.69608194801031
## 16 min thresh: 3.69608194801031
## 17 min thresh: 3.59018197598658
## 18 min thresh: 2.6278139927504
## 19 min thresh: 2.6278139927504
## 20 min thresh: 2.6278139927504
## 21 min thresh: 2.6278139927504
## 22 min thresh: 2.6278139927504
## 23 min thresh: 2.6278139927504
## 24 min thresh: 2.6278139927504
## 25 min thresh: 2.6278139927504
## 26 min thresh: 2.6278139927504
## 27 min thresh: 2.6278139927504
## 28 min thresh: 2.6278139927504
## 29 min thresh: 2.6278139927504
## 30 min thresh: 2.6278139927504
## 31 min thresh: 2.6278139927504
## 32 min thresh: 2.6184916161242
## 33 min thresh: 2.6184916161242
## 34 min thresh: 1.69655856614256
## 35 min thresh: 1.69655856614256
## 36 min thresh: 1.46711272102174
## 37 min thresh: 1.46711272102174
## 38 min thresh: 1.46711272102174
## 39 min thresh: 1.46711272102174
## 40 min thresh: 1.46711272102174
## 41 min thresh: 1.46711272102174
## 42 min thresh: 1.46711272102174
## 43 min thresh: 1.46711272102174
## 44 min thresh: 1.46711272102174
## 45 min thresh: 1.46711272102174
## 46 min thresh: 1.46711272102174
## 47 min thresh: 1.30294109227485
## 48 min thresh: 1.16045991904828
## 49 min thresh: 1.05957785544494
## 50 min thresh: 1.05957785544494
## 51 min thresh: 1.05957785544494
## 52 min thresh: 1.05957785544494
## 53 min thresh: 0.939744059116851
## 54 min thresh: 0.939744059116851
## 55 min thresh: 0.939744059116851
## 56 min thresh: 0.861904669338245
## 57 min thresh: 0.839269944153361
## 58 min thresh: 0.839269944153361
## 59 min thresh: 0.839269944153361
## 60 min thresh: 0.839269944153361
## 61 min thresh: 0.839269944153361
## 62 error: 59.33586 time: 0.19 secsplot(NN_HR2, cex=0.6, lwd=0.1)summary(NN_HR2$net.result)## Length Class Mode
## [1,] 2058 -none- numericNN_HR2$net.result ## [[1]]
## [,1]
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## 2940 0.024444529nrow(traindata_NN)## [1] 2058## Assigning the Probabilities to Dev Sample
traindata_NN$Prob = NN_HR2$net.result[[1]]
#traindata_NN$Prob = as.data.frame(traindata_NN$Prob)
summary(traindata_NN$Prob)## V1
## Min. :0.003683
## 1st Qu.:0.006299
## Median :0.017421
## Mean :0.154634
## 3rd Qu.:0.101291
## Max. :0.930017#View(traindata_NN)hist(traindata_NN$Prob, breaks=20)
## Assgining 0 / 1 class based on certain threshold
traindata_NN$Class = ifelse(traindata_NN$Prob>0.50,1,0)
tabtrain_NN = with( traindata_NN, table(Target_NN, as.factor(Class) ))
tabtrain_NN##
## Target_NN 0 1
## 0 1671 55
## 1 80 252TN_train_NN = tabtrain_NN[1,1]
TN_train_NN## [1] 1671TP_train_NN = tabtrain_NN[2,2]
TP_train_NN## [1] 252FN_train_NN = tabtrain_NN[2,1]
FN_train_NN## [1] 80FP_train_NN = tabtrain_NN[1,2]
FP_train_NN## [1] 55train_NN_acc = (TN_train_NN+TP_train_NN)/(TN_train_NN+TP_train_NN+FN_train_NN+FP_train_NN)
train_NN_acc## [1] 0.9344023train_NN_sens = TP_train_NN/(TP_train_NN+FN_train_NN)
train_NN_sens## [1] 0.7590361train_NN_spec = TN_train_NN/(TN_train_NN+FP_train_NN)
train_NN_spec## [1] 0.9681344#Predict for test data
names(testdata_NN)## [1] "Target_NN" "Age"
## [3] "BusinessTravelNon-Travel" "BusinessTravelTravel_Frequently"
## [5] "BusinessTravelTravel_Rarely" "DailyRate"
## [7] "DepartmentHumanResources" "DepartmentResearchDevelopment"
## [9] "DepartmentSales" "DistanceFromHome"
## [11] "Education" "EducationFieldHumanResources"
## [13] "EducationFieldLifeSciences" "EducationFieldMarketing"
## [15] "EducationFieldMedical" "EducationFieldOther"
## [17] "EducationFieldTechnicalDegree" "EnvironmentSatisfaction"
## [19] "GenderFemale" "GenderMale"
## [21] "HourlyRate" "JobInvolvement"
## [23] "JobLevel" "JobRoleHealthcareRepresentative"
## [25] "JobRoleHumanResources" "JobRoleLaboratoryTechnician"
## [27] "JobRoleManager" "JobRoleManufacturingDirector"
## [29] "JobRoleResearchDirector" "JobRoleResearchScientist"
## [31] "JobRoleSalesExecutive" "JobRoleSalesRepresentative"
## [33] "JobSatisfaction" "MaritalStatusDivorced"
## [35] "MaritalStatusMarried" "MaritalStatusSingle"
## [37] "MonthlyIncome" "MonthlyRate"
## [39] "NumCompaniesWorked" "OverTimeNo"
## [41] "OverTimeYes" "PercentSalaryHike"
## [43] "PerformanceRating" "RelationshipSatisfaction"
## [45] "StockOptionLevel" "TotalWorkingYears"
## [47] "TrainingTimesLastYear" "WorkLifeBalance"
## [49] "YearsAtCompany" "YearsInCurrentRole"
## [51] "YearsSinceLastPromotion" "YearsWithCurrManager"compute_test_NN = compute(NN_HR2, testdata_NN[,-1])
testdata_NN$Predict.score = compute_test_NN$net.result
# testdata_NN$Predict.score
testdata_NN$Class = ifelse(testdata_NN$Predict.score>0.50,1,0)
tabtest_NN = with( testdata_NN, table(Target_NN, as.factor(Class) ))
tabtest_NN##
## Target_NN 0 1
## 0 695 45
## 1 52 90TN_test_NN = tabtest_NN[1,1]
TN_test_NN## [1] 695TP_test_NN = tabtest_NN[2,2]
TP_test_NN## [1] 90FN_test_NN = tabtest_NN[2,1]
FN_test_NN## [1] 52FP_test_NN = tabtest_NN[1,2]
FP_test_NN## [1] 45test_NN_acc = (TN_test_NN+TP_test_NN)/(TN_test_NN+TP_test_NN+FN_test_NN+FP_test_NN)
test_NN_acc## [1] 0.8900227test_NN_sens = TP_test_NN/(TP_test_NN+FN_test_NN)
test_NN_sens## [1] 0.6338028test_NN_spec = TN_test_NN/(TN_test_NN+FP_test_NN)
test_NN_spec## [1] 0.9391892Compare Train and test results
df_results_train_NN = data.frame(train_NN_acc, train_NN_sens, train_NN_spec)
names(df_results_train_NN) = c("ACC", "SENS", "SPEC")
df_results_test_NN = data.frame(test_NN_acc, test_NN_sens, test_NN_spec)
names(df_results_test_NN) = c("ACC", "SENS", "SPEC")
?rbind
df_fin_NN =rbind(df_results_train_NN, df_results_test_NN)
row.names(df_fin_NN) = c('HR_Attrition_NN_train', 'HR_Attrition_NN_test')
round(df_fin_NN,2)## ACC SENS SPEC
## HR_Attrition_NN_train 0.93 0.76 0.97
## HR_Attrition_NN_test 0.89 0.63 0.94#install.packages("pROC")
library(pROC)## Warning: package 'pROC' was built under R version 3.6.1## Type 'citation("pROC")' for a citation.##
## Attaching package: 'pROC'## The following objects are masked from 'package:stats':
##
## cov, smooth, var#Train data
roc_obj_train_NN = roc(traindata_NN$Target_NN, traindata_NN$Prob[,1])## Setting levels: control = 0, case = 1## Setting direction: controls < casesroc_obj_train_NN##
## Call:
## roc.default(response = traindata_NN$Target_NN, predictor = traindata_NN$Prob[, 1])
##
## Data: traindata_NN$Prob[, 1] in 1726 controls (traindata_NN$Target_NN 0) < 332 cases (traindata_NN$Target_NN 1).
## Area under the curve: 0.899plot(roc_obj_train_NN, print.auc = T)
#traindata_NN$Prob[,1]
#test data
#testdata_NN$Predict.score
roc_obj_test_NN = roc(testdata_NN$Target_NN, testdata_NN$Predict.score[,1])## Setting levels: control = 0, case = 1
## Setting direction: controls < casesplot(roc_obj_test_NN, print.auc = T)
#testdata_NN$Prob[,1]